diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000..dd84ea7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/custom.md
new file mode 100644
index 0000000..48d5f81
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/custom.md
@@ -0,0 +1,10 @@
+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..bbcbbe7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..c77d007
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,12 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: "" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"
+
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
new file mode 100644
index 0000000..6305650
--- /dev/null
+++ b/.github/workflows/codeql.yml
@@ -0,0 +1,100 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL Advanced"
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+  schedule:
+    - cron: '16 4 * * 4'
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: actions
+          build-mode: none
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Add any setup steps before running the `github/codeql-action/init` action.
+    # This includes steps like installing compilers or runtimes (`actions/setup-node`
+    # or others). This is typically only required for manual builds.
+    # - name: Setup runtime (example)
+    #   uses: actions/setup-example@v1
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"
diff --git a/README.md b/README.md
index c65da3d..3ae4e5a 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ OpenTrustEval is a comprehensive, high-performance, and modular platform for AI
 ---
 
 ## 🛠️ Installation
-```bash
+bash
 git clone https://github.com/Kumarvels/OpenTrustEval.git
 cd OpenTrustEval
 python -m venv .venv
@@ -30,12 +30,12 @@ source .venv/bin/activate  # On Linux/Mac
 pip install -r requirements.txt
 ```
 
----
+
 
 ## 🌐 Launch the Unified WebUI
 The WebUI provides a single interface for LLM, Data, Security, and Research management.
 
-```bash
+bash
 streamlit run launch_workflow_webui.py
 ```
 - Open [http://localhost:8501](http://localhost:8501) in your browser.
@@ -112,5 +112,289 @@ python superfast_production_server.py
 
 ---
 
+## Why Trust-Based Systems Are Better (than Label Error Detection Systems) ?
+
+Let me break down the fundamental differences and explain why a trust-based approach is more comprehensive and valuable.
+
+## Core Philosophical Differences
+
+### **Label Error Detection System**
+```
+Focus: Data Quality → Model Performance
+Approach: Find and fix problems in training data
+Scope: Limited to labeled dataset issues
+Outcome: Better training data
+```
+
+### **Trust-Based System**
+```
+Focus: Holistic System Reliability → Real-World Performance
+Approach: Evaluate comprehensive trustworthiness
+Scope: End-to-end system behavior including deployment
+Outcome: Confidence in system behavior
+```
+
+## Detailed Comparison
+
+### 1. **Scope and Coverage**
+
+**Label Error Detection Limitations:**
+```python
+# CleanLab approach - focused on training data
+def cleanlab_approach(training_data, labels):
+    # Only addresses:
+    # 1. Mislabeling in training data
+    # 2. Data quality issues
+    # 3. Confidence in training predictions
+    
+    label_issues = find_label_errors(labels, pred_probs)
+    cleaned_data = remove_label_issues(training_data, label_issues)
+    return cleaned_data  # Better training data, but...
+    
+# What about deployment behavior? Real-world performance? 
+# These are NOT addressed by label error detection alone
+```
+
+**Trust-Based Approach:**
+```python
+# OpenTrustEval approach - comprehensive trust evaluation
+def trust_based_approach(model, training_data, test_data, production_data):
+    trust_assessment = {
+        # Training Data Quality (includes label error detection)
+        'data_quality': evaluate_data_quality(training_data, labels),
+        
+        # Model Reliability
+        'reliability': evaluate_reliability(model, test_data),
+        
+        # Consistency Across Inputs
+        'consistency': evaluate_consistency(model, various_inputs),
+        
+        # Fairness and Bias
+        'fairness': evaluate_fairness(model, diverse_test_cases),
+        
+        # Robustness to Adversarial Attacks
+        'robustness': evaluate_robustness(model, adversarial_examples),
+        
+        # Explainability and Transparency
+        'explainability': evaluate_explainability(model, inputs),
+        
+        # Production Behavior
+        'deployment_trust': evaluate_production_behavior(model, production_data)
+    }
+    
+    return comprehensive_trust_score(trust_assessment)
+```
+
+### 2. **Real-World Performance vs. Training Performance**
+
+**The Fundamental Problem:**
+```python
+# Scenario: Perfect training data, poor real-world trust
+class ExampleScenario:
+    def demonstrate_limitation(self):
+        # Training data is perfect (no label errors)
+        training_data_quality = 0.99  # CleanLab would be happy
+        
+        # But model has issues:
+        reliability_score = 0.6       # Unreliable predictions
+        consistency_score = 0.5       # Inconsistent responses
+        fairness_score = 0.4          # Biased decisions
+        robustness_score = 0.3        # Fragile to input changes
+        
+        # Label error detection says: "Data is clean!"
+        # Trust system says: "Don't deploy this - it's not trustworthy!"
+        
+        return {
+            'cleanlab_assessment': 'Data quality excellent',
+            'trust_assessment': 'System not ready for deployment'
+        }
+```
+
+### 3. **Temporal and Contextual Trust**
+
+**Label Error Detection Cannot Address:**
+```python
+# Issues that arise over time and context
+def temporal_trust_challenges():
+    return {
+        # Time-based issues (CleanLab can't detect):
+        'concept_drift': 'Model performance degrades as world changes',
+        'data_drift': 'Input distribution shifts in production',
+        'model_degradation': 'Performance naturally degrades over time',
+        
+        # Context-based issues:
+        'domain_adaptation': 'Works in training domain but fails in deployment domain',
+        'edge_cases': 'Handles common cases but fails on edge cases',
+        'user_trust': 'Users lose confidence due to inconsistent behavior'
+    }
+```
+
+## Why Trust-Based Systems Are Superior
+
+### 1. **Comprehensive Risk Assessment**
+
+**Trust systems evaluate:**
+```python
+def comprehensive_risk_assessment():
+    return {
+        # Pre-deployment risks (partially covered by CleanLab)
+        'training_data_risks': ['label_errors', 'bias', 'completeness'],
+        
+        # Model behavior risks (NOT covered by CleanLab)
+        'behavioral_risks': [
+            'overconfidence',           # Model too confident in wrong answers
+            'inconsistency',            # Different responses to similar inputs
+            'adversarial_vulnerability', # Security risks
+            'bias_amplification'        # Fairness issues in deployment
+        ],
+        
+        # Deployment risks (NOT covered by CleanLab)
+        'deployment_risks': [
+            'production_drift',         # Performance degradation over time
+            'user_acceptance',          # Human trust and adoption
+            'regulatory_compliance',    # Legal and ethical requirements
+            'business_impact'           # Real-world consequences of failures
+        ]
+    }
+```
+
+### 2. **Decision-Making Support**
+
+**Beyond Data Quality:**
+```python
+def decision_making_support():
+    # CleanLab helps answer: "Is my training data good?"
+    cleanlab_question = "Should I retrain with cleaned data?"
+    
+    # Trust systems help answer broader questions:
+    trust_questions = [
+        "Should I deploy this model to production?",
+        "Can I trust this model's decisions in critical situations?",
+        "How will this model perform with real users?",
+        "What are the risks of deploying this system?",
+        "How can I improve overall system trustworthiness?"
+    ]
+    
+    return {
+        'cleanlab_scope': cleanlab_question,
+        'trust_scope': trust_questions
+    }
+```
+
+### 3. **Continuous Monitoring and Improvement**
+
+**Evolution Over Time:**
+```python
+def evolution_comparison():
+    return {
+        'label_error_detection': {
+            'phase': 'Training/pre-deployment',
+            'frequency': 'One-time or periodic retraining',
+            'scope': 'Static training dataset',
+            'outcome': 'Better training data'
+        },
+        
+        'trust_based_system': {
+            'phase': 'End-to-end lifecycle (training → deployment → monitoring)',
+            'frequency': 'Continuous monitoring',
+            'scope': 'Dynamic system behavior in real-world conditions',
+            'outcome': 'Confidence in system reliability and safety'
+        }
+    }
+```
+
+## Concrete Examples Where Trust Systems Excel
+
+### Example 1: **Medical Diagnosis System**
+
+```python
+# CleanLab approach:
+medical_model_cleanlab = {
+    'training_data_quality': 0.98,  # Very clean data
+    'recommendation': 'Ready for deployment'
+}
+
+# Trust-based approach:
+medical_model_trust = {
+    'training_data_quality': 0.98,     # Same clean data
+    'reliability_score': 0.7,          # Sometimes confident when wrong
+    'consistency_score': 0.6,          # Different diagnoses for similar symptoms
+    'robustness_score': 0.5,           # Fragile to slight input variations
+    'fairness_score': 0.8,             # Good but not perfect
+    'explainability_score': 0.4,       # Poor explanations for decisions
+    'overall_trust': 0.6,              # NOT ready for deployment!
+    'recommendation': 'Needs significant improvement before deployment'
+}
+```
+
+### Example 2: **Autonomous Vehicle Perception**
+
+```python
+# CleanLab approach:
+av_perception_cleanlab = {
+    'training_data_quality': 0.95,  # Good object detection labels
+    'recommendation': 'Good data quality'
+}
+
+# Trust-based approach:
+av_perception_trust = {
+    'training_data_quality': 0.95,     # Same good data
+    'reliability_in_rain': 0.3,        # Terrible in rain conditions
+    'consistency_at_night': 0.4,       # Inconsistent night performance
+    'robustness_to_adversarial': 0.2,  # Vulnerable to simple attacks
+    'edge_case_handling': 0.3,         # Fails on unusual scenarios
+    'safety_trust': 0.3,               # DANGEROUS for deployment!
+    'recommendation': 'Absolutely not ready - safety risks too high'
+}
+```
+
+## The Trust Advantage: Beyond Binary Decisions
+
+### **CleanLab's Binary Thinking:**
+```
+Data Quality: Good/Bad → Retrain/Don't Retrain
+```
+
+### **Trust-Based Thinking:**
+```
+Trust Dimensions:
+├── Reliability: 0.7 (Moderate confidence)
+├── Consistency: 0.6 (Some variability acceptable)
+├── Fairness: 0.9 (Excellent)
+├── Robustness: 0.4 (Needs improvement)
+├── Explainability: 0.8 (Good)
+└── Overall Trust: 0.6 (Improvement needed)
+
+Decision Matrix:
+├── Critical Applications: DON'T DEPLOY
+├── Low-Stakes Applications: DEPLOY with monitoring
+└── Research Applications: DEPLOY with caveats
+```
+
+## Fundamental Truth
+
+**Perfect training data ≠ Trustworthy system**
+
+A trust-based system recognizes that:
+1. **Data quality is necessary but not sufficient** for trustworthy AI
+2. **Model behavior in deployment matters more** than training data quality
+3. **Human trust and acceptance** are crucial for real-world success
+4. **Continuous monitoring and improvement** are essential for long-term success
+
+## Conclusion
+
+Trust-based systems are superior because they:
+
+1. **Provide comprehensive assessment** beyond just data quality
+2. **Support better decision-making** for real-world deployment
+3. **Consider end-to-end system behavior** rather than isolated components
+4. **Enable continuous improvement** throughout the AI lifecycle
+5. **Address human factors** like user trust and acceptance
+6. **Prepare for real-world complexity** rather than controlled environments
+
+While label error detection is valuable (and should be part of any comprehensive approach), it's only one piece of the much larger trust puzzle. 
+A trust-based system provides the holistic view needed to build truly reliable, safe, and successful AI systems.
+
+
 ## 🏆 Credits
 OpenTrustEval is developed and maintained by Kumarvels and contributors. For issues, feature requests, or contributions, please open an issue or pull request on GitHub.
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000..034e848
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,21 @@
+# Security Policy
+
+## Supported Versions
+
+Use this section to tell people about which versions of your project are
+currently being supported with security updates.
+
+| Version | Supported          |
+| ------- | ------------------ |
+| 5.1.x   | :white_check_mark: |
+| 5.0.x   | :x:                |
+| 4.0.x   | :white_check_mark: |
+| < 4.0   | :x:                |
+
+## Reporting a Vulnerability
+
+Use this section to tell people how to report a vulnerability.
+
+Tell them where to go, how often they can expect to get an update on a
+reported vulnerability, what to expect if the vulnerability is accepted or
+declined, etc.
diff --git a/cloudscale_apis/endpoints/trust_api.py b/cloudscale_apis/endpoints/trust_api.py
new file mode 100644
index 0000000..b1c18a8
--- /dev/null
+++ b/cloudscale_apis/endpoints/trust_api.py
@@ -0,0 +1,338 @@
+# src/api/trust_api.py
+"""
+Trust API and Microservices - Scalable trust evaluation services
+"""
+
+from fastapi import FastAPI, HTTPException, BackgroundTasks
+from pydantic import BaseModel, Field
+from typing import Dict, Any, List, Optional
+import asyncio
+import uuid
+from datetime import datetime
+import redis
+import json
+import logging
+
+logger = logging.getLogger(__name__)
+
+# FastAPI app
+app = FastAPI(
+    title="OpenTrustEval API",
+    description="Scalable trust evaluation API with microservices architecture",
+    version="1.0.0"
+)
+
+# Redis for caching and job queue
+redis_client = redis.Redis(host='localhost', port=6379, db=0)
+
+# Pydantic models
+class TrustEvaluationRequest(BaseModel):
+    model_id: str = Field(..., description="Unique identifier for the model")
+    model_type: str = Field(default="llm", description="Type of model (llm, ml, cv, etc.)")
+    data: Dict[str, Any] = Field(..., description="Evaluation data")
+    evaluation_config: Optional[Dict[str, Any]] = Field(default={}, description="Evaluation configuration")
+    callback_url: Optional[str] = Field(default=None, description="URL for callback notifications")
+
+class TrustEvaluationResponse(BaseModel):
+    evaluation_id: str
+    status: str
+    results: Optional[Dict[str, Any]] = None
+    error: Optional[str] = None
+    created_at: datetime
+    completed_at: Optional[datetime] = None
+
+class TrustEvaluationStatus(BaseModel):
+    evaluation_id: str
+    status: str
+    progress: Optional[float] = None
+    estimated_completion: Optional[datetime] = None
+
+# In-memory storage for demonstration (use database in production)
+evaluation_storage = {}
+
+# Trust evaluation service
+class TrustEvaluationService:
+    """Service for trust evaluation operations"""
+    
+    @staticmethod
+    async def execute_evaluation(request: TrustEvaluationRequest) -> Dict[str, Any]:
+        """Execute trust evaluation"""
+        try:
+            # Import evaluator (lazy import for performance)
+            from src.evaluators.composite_evaluator import CompositeTrustEvaluator
+            
+            # Create evaluator
+            evaluator = CompositeTrustEvaluator()
+            
+            # Execute evaluation
+            results = evaluator.evaluate_comprehensive_trust(
+                model=None,  # In real implementation, load model by model_id
+                data=request.data,
+                model_type=request.model_type,
+                **request.evaluation_config
+            )
+            
+            return {
+                'status': 'completed',
+                'results': results,
+                'completed_at': datetime.now()
+            }
+            
+        except Exception as e:
+            logger.error(f"Evaluation failed: {e}")
+            return {
+                'status': 'failed',
+                'error': str(e),
+                'completed_at': datetime.now()
+            }
+    
+    @staticmethod
+    async def queue_evaluation(request: TrustEvaluationRequest) -> str:
+        """Queue evaluation for background processing"""
+        evaluation_id = str(uuid.uuid4())
+        
+        # Store initial request
+        evaluation_storage[evaluation_id] = {
+            'request': request.dict(),
+            'status': 'queued',
+            'created_at': datetime.now()
+        }
+        
+        # Add to Redis queue
+        job_data = {
+            'evaluation_id': evaluation_id,
+            'request': request.dict()
+        }
+        redis_client.lpush('trust_evaluation_queue', json.dumps(job_data))
+        
+        return evaluation_id
+
+# API endpoints
+@app.post("/evaluate", response_model=TrustEvaluationResponse)
+async def evaluate_trust(request: TrustEvaluationRequest, background_tasks: BackgroundTasks):
+    """Submit trust evaluation request"""
+    try:
+        # Queue for background processing
+        evaluation_id = await TrustEvaluationService.queue_evaluation(request)
+        
+        return TrustEvaluationResponse(
+            evaluation_id=evaluation_id,
+            status="queued",
+            created_at=evaluation_storage[evaluation_id]['created_at']
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/evaluation/{evaluation_id}", response_model=TrustEvaluationResponse)
+async def get_evaluation_status(evaluation_id: str):
+    """Get evaluation status and results"""
+    if evaluation_id not in evaluation_storage:
+        raise HTTPException(status_code=404, detail="Evaluation not found")
+    
+    stored_data = evaluation_storage[evaluation_id]
+    
+    return TrustEvaluationResponse(
+        evaluation_id=evaluation_id,
+        status=stored_data['status'],
+        results=stored_data.get('results'),
+        error=stored_data.get('error'),
+        created_at=stored_data['created_at'],
+        completed_at=stored_data.get('completed_at')
+    )
+
+@app.get("/evaluation/{evaluation_id}/status", response_model=TrustEvaluationStatus)
+async def get_evaluation_progress(evaluation_id: str):
+    """Get evaluation progress"""
+    if evaluation_id not in evaluation_storage:
+        raise HTTPException(status_code=404, detail="Evaluation not found")
+    
+    stored_data = evaluation_storage[evaluation_id]
+    
+    return TrustEvaluationStatus(
+        evaluation_id=evaluation_id,
+        status=stored_data['status'],
+        progress=stored_data.get('progress', 0.0) if stored_data['status'] == 'processing' else 
+                (1.0 if stored_data['status'] == 'completed' else 0.0)
+    )
+
+# Background worker
+async def trust_evaluation_worker():
+    """Background worker for processing trust evaluations"""
+    logger.info("Starting trust evaluation worker")
+    
+    while True:
+        try:
+            # Get job from Redis queue
+            job_data_json = redis_client.brpop('trust_evaluation_queue', timeout=1)
+            
+            if job_data_json:
+                _, job_data_bytes = job_data_json
+                job_data = json.loads(job_data_bytes.decode('utf-8'))
+                
+                evaluation_id = job_data['evaluation_id']
+                request_data = job_data['request']
+                
+                logger.info(f"Processing evaluation {evaluation_id}")
+                
+                # Update status
+                evaluation_storage[evaluation_id]['status'] = 'processing'
+                evaluation_storage[evaluation_id]['progress'] = 0.1
+                
+                # Execute evaluation
+                request = TrustEvaluationRequest(**request_data)
+                results = await TrustEvaluationService.execute_evaluation(request)
+                
+                # Update storage
+                evaluation_storage[evaluation_id].update(results)
+                evaluation_storage[evaluation_id]['status'] = results['status']
+                
+                # Trigger callback if provided
+                if request.callback_url:
+                    await trigger_callback(request.callback_url, evaluation_id, results)
+                
+                logger.info(f"Completed evaluation {evaluation_id}")
+            
+            await asyncio.sleep(0.1)  # Prevent busy waiting
+            
+        except Exception as e:
+            logger.error(f"Worker error: {e}")
+            await asyncio.sleep(1)  # Slow down on errors
+
+async def trigger_callback(callback_url: str, evaluation_id: str, results: Dict[str, Any]):
+    """Trigger callback notification"""
+    try:
+        import httpx
+        async with httpx.AsyncClient() as client:
+            callback_data = {
+                'evaluation_id': evaluation_id,
+                'results': results
+            }
+            await client.post(callback_url, json=callback_data)
+    except Exception as e:
+        logger.error(f"Callback failed: {e}")
+
+# Batch evaluation endpoints
+class BatchEvaluationRequest(BaseModel):
+    evaluations: List[TrustEvaluationRequest]
+    batch_config: Optional[Dict[str, Any]] = Field(default={}, description="Batch processing configuration")
+
+class BatchEvaluationResponse(BaseModel):
+    batch_id: str
+    status: str
+    completed_evaluations: int = 0
+    total_evaluations: int
+    results: Optional[List[Dict[str, Any]]] = None
+
+@app.post("/batch-evaluate", response_model=BatchEvaluationResponse)
+async def batch_evaluate(request: BatchEvaluationRequest, background_tasks: BackgroundTasks):
+    """Submit batch trust evaluation request"""
+    batch_id = str(uuid.uuid4())
+    
+    # Queue individual evaluations
+    evaluation_ids = []
+    for eval_request in request.evaluations:
+        eval_id = await TrustEvaluationService.queue_evaluation(eval_request)
+        evaluation_ids.append(eval_id)
+    
+    # Store batch information
+    batch_storage[batch_id] = {
+        'evaluation_ids': evaluation_ids,
+        'status': 'processing',
+        'total_evaluations': len(evaluation_ids),
+        'completed_evaluations': 0,
+        'created_at': datetime.now()
+    }
+    
+    # Start batch monitoring in background
+    background_tasks.add_task(monitor_batch_progress, batch_id, evaluation_ids)
+    
+    return BatchEvaluationResponse(
+        batch_id=batch_id,
+        status="processing",
+        total_evaluations=len(evaluation_ids)
+    )
+
+# Batch storage
+batch_storage = {}
+
+async def monitor_batch_progress(batch_id: str, evaluation_ids: List[str]):
+    """Monitor batch evaluation progress"""
+    while batch_storage[batch_id]['completed_evaluations'] < batch_storage[batch_id]['total_evaluations']:
+        completed_count = 0
+        for eval_id in evaluation_ids:
+            if eval_id in evaluation_storage:
+                if evaluation_storage[eval_id]['status'] in ['completed', 'failed']:
+                    completed_count += 1
+        
+        batch_storage[batch_id]['completed_evaluations'] = completed_count
+        
+        if completed_count == len(evaluation_ids):
+            batch_storage[batch_id]['status'] = 'completed'
+            # Collect results
+            results = []
+            for eval_id in evaluation_ids:
+                if eval_id in evaluation_storage:
+                    results.append(evaluation_storage[eval_id])
+            batch_storage[batch_id]['results'] = results
+            break
+        
+        await asyncio.sleep(5)  # Check every 5 seconds
+
+# Health check endpoint
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "timestamp": datetime.now().isoformat(),
+        "services": {
+            "api": "running",
+            "worker": "running" if is_worker_alive() else "stopped",
+            "redis": "connected" if is_redis_connected() else "disconnected"
+        }
+    }
+
+def is_worker_alive() -> bool:
+    """Check if worker is alive"""
+    # Implementation would check worker status
+    return True
+
+def is_redis_connected() -> bool:
+    """Check Redis connection"""
+    try:
+        redis_client.ping()
+        return True
+    except:
+        return False
+
+# Startup and shutdown events
+@app.on_event("startup")
+async def startup_event():
+    """Startup event handler"""
+    logger.info("Starting Trust API service")
+    
+    # Start background worker
+    asyncio.create_task(trust_evaluation_worker())
+    
+    logger.info("Trust API service started")
+
+@app.on_event("shutdown")
+async def shutdown_event():
+    """Shutdown event handler"""
+    logger.info("Shutting down Trust API service")
+
+# CLI for API management
+def start_api_server():
+    """Start API server"""
+    import uvicorn
+    uvicorn.run(
+        "src.api.trust_api:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=True,
+        log_level="info"
+    )
+
+if __name__ == "__main__":
+    start_api_server()
diff --git a/data_engineering/dataset_integration.py b/data_engineering/dataset_integration.py
index ec78c52..34e0059 100644
--- a/data_engineering/dataset_integration.py
+++ b/data_engineering/dataset_integration.py
@@ -15,6 +15,7 @@
 from datetime import datetime
 import hashlib
 
+import re
 # Optional imports for advanced features
 try:
     import plotly.express as px
@@ -298,6 +299,28 @@ def create_quality_filtered_dataset(self, dataset_id: str, min_trust_score: floa
             self.logger.error(f"Error creating quality-filtered dataset: {e}")
             raise
     
+    def is_safe_query(self, query_str: str, allowed_columns) -> bool:
+        """
+        Check if the query string is safe: only allowed column names, numbers, and safe operators.
+        """
+        # Only allow column names, numbers, whitespace, and safe operators
+        # Disallow parentheses, function calls, __import__, etc.
+        # Allowed operators: ==, !=, <, >, <=, >=, and, or, not
+        # Build regex for allowed columns
+        col_pattern = r'|'.join([re.escape(col) for col in allowed_columns])
+        # Full pattern: allowed columns, numbers, operators, whitespace
+        safe_pattern = rf'^([\s\d\.\'"]*({col_pattern})[\s\d\.\'"]*(==|!=|<=|>=|<|>|and|or|not|&|\||\s)*[\s\d\.\'"]*)+$'
+        # Disallow suspicious keywords
+        forbidden = ['__import__', 'os.', 'sys.', 'eval', 'exec', 'open(', '(', ')', '[', ']', '{', '}', ';']
+        lowered = query_str.lower()
+        for word in forbidden:
+            if word in lowered:
+                return False
+        # Check regex
+        if re.match(safe_pattern, query_str):
+            return True
+        return False
+
     def process_dataset(self, dataset_id: str, transformations: List[Dict]) -> str:
         """
         Apply transformations to a dataset
@@ -322,7 +345,28 @@ def process_dataset(self, dataset_id: str, transformations: List[Dict]) -> str:
             elif operation == 'rename_columns':
                 df = df.rename(columns=params['mapping'])
             elif operation == 'filter':
-                df = df.query(params['condition'])
+                # Expect params: {'column': ..., 'operator': ..., 'value': ...}
+                column = params.get('column')
+                operator = params.get('operator')
+                value = params.get('value')
+                allowed_operators = ['==', '!=', '<', '>', '<=', '>=']
+                if column not in df.columns:
+                    raise ValueError(f"Column '{column}' not found in dataset.")
+                if operator not in allowed_operators:
+                    raise ValueError(f"Operator '{operator}' is not allowed.")
+                # Apply filter using boolean indexing
+                if operator == '==':
+                    df = df[df[column] == value]
+                elif operator == '!=':
+                    df = df[df[column] != value]
+                elif operator == '<':
+                    df = df[df[column] < value]
+                elif operator == '>':
+                    df = df[df[column] > value]
+                elif operator == '<=':
+                    df = df[df[column] <= value]
+                elif operator == '>=':
+                    df = df[df[column] >= value]
             elif operation == 'sort':
                 df = df.sort_values(by=params['columns'], ascending=params.get('ascending', True))
             elif operation == 'groupby':
diff --git a/data_engineering/requirements_dashboard.txt b/data_engineering/requirements_dashboard.txt
index c0f7a55..f7e9d7b 100644
--- a/data_engineering/requirements_dashboard.txt
+++ b/data_engineering/requirements_dashboard.txt
@@ -9,7 +9,7 @@ streamlit>=1.28.0
 plotly>=5.15.0
 
 # Database
-sqlite3  # Usually included with Python
+# sqlite3  # Usually included with Python
 
 # Optional: For enhanced features
 # scikit-learn>=1.1.0  # For advanced trust scoring
diff --git a/data_engineering/scripts/easy_dataset_webui.py b/data_engineering/scripts/easy_dataset_webui.py
index eedfa29..3828e04 100644
--- a/data_engineering/scripts/easy_dataset_webui.py
+++ b/data_engineering/scripts/easy_dataset_webui.py
@@ -248,7 +248,7 @@ def run_cleanlab_on_dataset(dataset_id, label_column, save_output):
         process_id = gr.Textbox(label="Dataset ID", placeholder="Enter dataset ID")
         transformations = gr.Textbox(
             label="Transformations (JSON)",
-            placeholder='[{"operation": "filter", "params": {"condition": "age > 30"}}]',
+            placeholder='[{"operation": "filter", "params": {"column": "age", "operator": ">", "value": 30}}]',
             lines=5
         )
         process_btn = gr.Button("Process Dataset")
diff --git a/high_performance_system/core/README.md b/high_performance_system/core/README.md
index ee9a473..9f9f24d 100644
--- a/high_performance_system/core/README.md
+++ b/high_performance_system/core/README.md
@@ -1,4 +1,4 @@
-# 🧠 high_performance_system/core – Functional, Technical & System Design
+# 🧠 High_performance_system/core – Functional, Technical & System Design
 
 ---
 
@@ -119,6 +119,51 @@ flowchart TD
 - **Secure:** PII and compliance are first-class concerns.
 - **Extensible:** New modules can be added with minimal changes.
 
+---
+---
+---
+---
+
+ Summary of Revolutionary Features
+---
+1. Trust Evolution Timeline
+Track trust changes over time
+Predict future trust trajectories
+Detect anomalies in trust patterns
+
+2. Cross-Model Correlation Engine
+Understand how trust in one model affects others
+Identify risk clusters and propagation paths
+System-level trust assessment
+
+3. Trust Decision Matrix
+Customizable trust criteria for different stakeholders
+Multi-profile evaluation for various contexts
+Automated deployment decisions based on trust
+
+4. Trust Simulation and Stress Testing
+Adversarial attack simulation
+Data drift testing
+Robustness scoring and recommendations
+
+5. Trust Orchestration Pipeline
+End-to-end trust evaluation pipeline
+Environment-specific deployment with monitoring
+Automated rollback on trust degradation
+
+6. Trust API and Microservices
+Scalable REST API for trust evaluation
+Background job processing
+Batch evaluation capabilities
+
+7. Trust Visualization Dashboard
+Interactive radar charts and timelines
+Risk matrices and correlation heatmaps
+Real-time monitoring with WebSocket updates
+
+Above 7 features transform OpenTrustEval from a simple evaluation tool into a comprehensive trust management platform that addresses the 
+real-world challenges of deploying trustworthy AI systems at scale.
+
 ---
 
-For more details, see the code and docstrings in each file, and refer to the main system README for integration and usage examples. 
\ No newline at end of file
+For more details, see the code and docstrings in each file, and refer to the main system README for integration and usage examples. 
diff --git a/high_performance_system/core/cross_model_correlation.py b/high_performance_system/core/cross_model_correlation.py
new file mode 100644
index 0000000..9b92a93
--- /dev/null
+++ b/high_performance_system/core/cross_model_correlation.py
@@ -0,0 +1,258 @@
+# src/correlation/cross_model_correlation.py
+"""
+Cross-Model Trust Correlation Engine - Understand how trust in one model affects others
+"""
+
+import numpy as np
+from scipy.stats import pearsonr
+from typing import Dict, Any, List, Tuple
+import networkx as nx
+import plotly.graph_objects as go
+
+class CrossModelCorrelationEngine:
+    """Analyzes trust correlations between different AI models"""
+    
+    def __init__(self):
+        self.model_network = nx.DiGraph()  # Directed graph for causality
+        self.trust_correlations = {}
+        self.interference_patterns = {}
+    
+    def register_model_relationship(self, source_model: str, target_model: str, 
+                                  relationship_type: str, strength: float = 1.0):
+        """Register a relationship between models"""
+        self.model_network.add_edge(source_model, target_model, 
+                                  relationship=relationship_type, 
+                                  strength=strength)
+    
+    def analyze_trust_interference(self, model_evaluations: Dict[str, Dict]) -> Dict[str, Any]:
+        """Analyze how trust in one model affects trust in others"""
+        interference_analysis = {}
+        
+        # Calculate correlation matrix
+        model_names = list(model_evaluations.keys())
+        trust_scores = {model: eval_data.get('overall_trust_score', 0.5) 
+                       for model, eval_data in model_evaluations.items()}
+        
+        # Pairwise correlation analysis
+        correlations = {}
+        for i, model1 in enumerate(model_names):
+            for j, model2 in enumerate(model_names):
+                if i != j:
+                    score1 = trust_scores[model1]
+                    score2 = trust_scores[model2]
+                    
+                    # Simple correlation (in real implementation, use more sophisticated methods)
+                    correlation = self._calculate_trust_correlation(
+                        model_evaluations[model1], 
+                        model_evaluations[model2]
+                    )
+                    
+                    correlations[f"{model1}__{model2}"] = {
+                        'correlation': correlation,
+                        'trust_impact': self._calculate_trust_impact(score1, score2),
+                        'risk_amplification': self._calculate_risk_amplification(
+                            model_evaluations[model1], 
+                            model_evaluations[model2]
+                        )
+                    }
+        
+        # Identify risk clusters
+        risk_clusters = self._identify_risk_clusters(model_evaluations)
+        
+        # Calculate system-level trust
+        system_trust = self._calculate_system_level_trust(model_evaluations)
+        
+        return {
+            'pairwise_correlations': correlations,
+            'risk_clusters': risk_clusters,
+            'system_level_trust': system_trust,
+            'vulnerability_analysis': self._analyze_vulnerability_propagation(model_evaluations),
+            'recommendations': self._generate_correlation_recommendations(
+                correlations, risk_clusters, system_trust
+            )
+        }
+    
+    def _calculate_trust_correlation(self, eval1: Dict, eval2: Dict) -> float:
+        """Calculate trust correlation between two model evaluations"""
+        # Extract dimension scores
+        dims1 = eval1.get('dimension_scores', {})
+        dims2 = eval2.get('dimension_scores', {})
+        
+        # Get common dimensions
+        common_dims = set(dims1.keys()) & set(dims2.keys())
+        
+        if len(common_dims) < 2:
+            return 0.0
+        
+        scores1 = [dims1[dim] for dim in common_dims]
+        scores2 = [dims2[dim] for dim in common_dims]
+        
+        # Calculate Pearson correlation
+        try:
+            correlation, _ = pearsonr(scores1, scores2)
+            return float(correlation)
+        except:
+            return 0.0
+    
+    def _calculate_trust_impact(self, source_trust: float, target_trust: float) -> Dict[str, float]:
+        """Calculate how source model trust impacts target model trust"""
+        # Simple impact model - in practice, this would be more sophisticated
+        impact_magnitude = abs(source_trust - 0.5) * abs(target_trust - 0.5) * 2
+        impact_direction = 1 if (source_trust > 0.5 and target_trust > 0.5) or \
+                              (source_trust < 0.5 and target_trust < 0.5) else -1
+        
+        return {
+            'magnitude': impact_magnitude,
+            'direction': impact_direction,  # 1 = positive correlation, -1 = negative
+            'risk_factor': impact_magnitude * abs(impact_direction)
+        }
+    
+    def _calculate_risk_amplification(self, eval1: Dict, eval2: Dict) -> float:
+        """Calculate how risks in one model amplify risks in another"""
+        # Extract risk scores
+        risks1 = eval1.get('risk_assessment', {}).get('high_risks', [])
+        risks2 = eval2.get('risk_assessment', {}).get('high_risks', [])
+        
+        # Simple risk amplification model
+        base_risk = len(risks1) + len(risks2)
+        amplified_risk = base_risk * (1 + len(risks1) * len(risks2) * 0.1)
+        
+        return min(10.0, amplified_risk)  # Cap at reasonable level
+    
+    def _identify_risk_clusters(self, model_evaluations: Dict[str, Dict]) -> List[Dict]:
+        """Identify clusters of models with correlated risks"""
+        # Simple clustering based on correlation thresholds
+        clusters = []
+        processed_models = set()
+        
+        for model_name, eval_data in model_evaluations.items():
+            if model_name in processed_models:
+                continue
+            
+            # Find correlated models
+            correlated_models = []
+            for other_model, other_eval in model_evaluations.items():
+                if other_model != model_name and other_model not in processed_models:
+                    correlation = self._calculate_trust_correlation(eval_data, other_eval)
+                    if abs(correlation) > 0.7:  # High correlation threshold
+                        correlated_models.append(other_model)
+                        processed_models.add(other_model)
+            
+            if correlated_models:
+                correlated_models.append(model_name)
+                clusters.append({
+                    'models': correlated_models,
+                    'cluster_risk': self._calculate_cluster_risk(
+                        {m: model_evaluations[m] for m in correlated_models}
+                    )
+                })
+                processed_models.add(model_name)
+        
+        return clusters
+    
+    def _calculate_system_level_trust(self, model_evaluations: Dict[str, Dict]) -> float:
+        """Calculate overall system trust considering correlations"""
+        if not model_evaluations:
+            return 0.5
+        
+        individual_trusts = [eval_data.get('overall_trust_score', 0.5) 
+                           for eval_data in model_evaluations.values()]
+        
+        # Simple average - in practice, weight by model importance and correlations
+        return float(np.mean(individual_trusts))
+    
+    def _analyze_vulnerability_propagation(self, model_evaluations: Dict[str, Dict]) -> Dict[str, Any]:
+        """Analyze how vulnerabilities might propagate through the system"""
+        propagation_analysis = {}
+        
+        for model_name, eval_data in model_evaluations.items():
+            vulnerabilities = eval_data.get('risk_assessment', {}).get('critical_risks', [])
+            
+            if vulnerabilities:
+                propagation_analysis[model_name] = {
+                    'vulnerabilities': vulnerabilities,
+                    'propagation_risk': len(vulnerabilities) * 0.2,
+                    'affected_downstream': self._find_affected_models(model_name),
+                    'mitigation_priority': self._calculate_mitigation_priority(eval_data)
+                }
+        
+        return propagation_analysis
+    
+    def _find_affected_models(self, source_model: str) -> List[str]:
+        """Find models that might be affected by issues in source model"""
+        if source_model in self.model_network:
+            return list(self.model_network.successors(source_model))
+        return []
+    
+    def _calculate_mitigation_priority(self, eval_data: Dict) -> str:
+        """Calculate priority for mitigation based on risk severity"""
+        critical_risks = len(eval_data.get('risk_assessment', {}).get('critical_risks', []))
+        high_risks = len(eval_data.get('risk_assessment', {}).get('high_risks', []))
+        
+        risk_score = critical_risks * 3 + high_risks
+        
+        if risk_score >= 6:
+            return 'critical'
+        elif risk_score >= 3:
+            return 'high'
+        elif risk_score >= 1:
+            return 'medium'
+        else:
+            return 'low'
+    
+    def _generate_correlation_recommendations(self, correlations: Dict, 
+                                            clusters: List[Dict], 
+                                            system_trust: float) -> List[str]:
+        """Generate recommendations based on correlation analysis"""
+        recommendations = []
+        
+        # System-level recommendations
+        if system_trust < 0.6:
+            recommendations.append("System-level trust is low. Consider comprehensive system review.")
+        
+        # Cluster recommendations
+        high_risk_clusters = [c for c in clusters if c['cluster_risk'] > 0.7]
+        if high_risk_clusters:
+            recommendations.append(f"Identified {len(high_risk_clusters)} high-risk model clusters. Review interdependencies.")
+        
+        # Correlation recommendations
+        strong_correlations = [corr for corr, data in correlations.items() 
+                              if abs(data['correlation']) > 0.8]
+        if strong_correlations:
+            recommendations.append(f"Found {len(strong_correlations)} strong model correlations. Monitor jointly.")
+        
+        return recommendations
+
+# Integration example
+class CorrelationAwareEvaluator:
+    """Evaluator that considers cross-model correlations"""
+    
+    def __init__(self):
+        self.correlation_engine = CrossModelCorrelationEngine()
+        # ... other initialization
+    
+    def evaluate_multi_model_system(self, models: Dict[str, Any], 
+                                  shared_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Evaluate a system with multiple interacting models"""
+        
+        # Evaluate each model individually
+        individual_evaluations = {}
+        for model_name, model in models.items():
+            individual_evaluations[model_name] = self.evaluate_comprehensive_trust(
+                model, shared_data.get(model_name, shared_data)
+            )
+        
+        # Analyze cross-model correlations
+        correlation_analysis = self.correlation_engine.analyze_trust_interference(
+            individual_evaluations
+        )
+        
+        return {
+            'individual_evaluations': individual_evaluations,
+            'correlation_analysis': correlation_analysis,
+            'system_overview': {
+                'total_models': len(models),
+                'system_trust': correlation_analysis['system_level_trust'],
+                'risk_clusters': len(correlation_analysis['risk_clusters'])
+            }
+        }
diff --git a/high_performance_system/core/trust_dashboard.py b/high_performance_system/core/trust_dashboard.py
new file mode 100644
index 0000000..33ed634
--- /dev/null
+++ b/high_performance_system/core/trust_dashboard.py
@@ -0,0 +1,429 @@
+# src/visualization/trust_dashboard.py
+"""
+Trust Visualization and Dashboard System - Interactive trust monitoring and analysis
+"""
+
+import dash
+from dash import dcc, html, Input, Output, callback
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import json
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Initialize Dash app
+app = dash.Dash(__name__, title="Trust Evaluation Dashboard")
+
+class TrustVisualizationEngine:
+    """Engine for creating trust visualizations"""
+    
+    @staticmethod
+    def create_trust_radar_chart(dimension_scores: Dict[str, float], 
+                               title: str = "Trust Dimensions") -> go.Figure:
+        """Create radar chart for trust dimensions"""
+        categories = list(dimension_scores.keys())
+        values = list(dimension_scores.values())
+        
+        # Close the radar chart
+        categories.append(categories[0])
+        values.append(values[0])
+        
+        fig = go.Figure()
+        fig.add_trace(go.Scatterpolar(
+            r=values,
+            theta=categories,
+            fill='toself',
+            name=title
+        ))
+        
+        fig.update_layout(
+            polar=dict(
+                radialaxis=dict(
+                    visible=True,
+                    range=[0, 1]
+                )
+            ),
+            showlegend=False,
+            title=title
+        )
+        
+        return fig
+    
+    @staticmethod
+    def create_trust_timeline(history_data: pd.DataFrame) -> go.Figure:
+        """Create timeline of trust scores"""
+        fig = go.Figure()
+        
+        # Overall trust score timeline
+        fig.add_trace(go.Scatter(
+            x=history_data['timestamp'],
+            y=history_data['overall_trust'],
+            mode='lines+markers',
+            name='Overall Trust',
+            line=dict(color='blue')
+        ))
+        
+        # Add dimension scores if available
+        if 'dimensions' in history_data.columns:
+            sample_dims = history_data.iloc[0]['dimensions']
+            for dim_name in sample_dims.keys():
+                dim_values = [dims.get(dim_name, 0.5) for dims in history_data['dimensions']]
+                fig.add_trace(go.Scatter(
+                    x=history_data['timestamp'],
+                    y=dim_values,
+                    mode='lines',
+                    name=f'{dim_name}',
+                    line=dict(dash='dot')
+                ))
+        
+        fig.update_layout(
+            title='Trust Evolution Over Time',
+            xaxis_title='Time',
+            yaxis_title='Trust Score',
+            yaxis=dict(range=[0, 1])
+        )
+        
+        return fig
+    
+    @staticmethod
+    def create_trust_heatmap(correlation_data: Dict[str, Dict[str, float]]) -> go.Figure:
+        """Create heatmap of trust correlations"""
+        # Convert to matrix format
+        dimensions = list(correlation_data.keys())
+        correlation_matrix = []
+        
+        for dim1 in dimensions:
+            row = []
+            for dim2 in dimensions:
+                row.append(correlation_data[dim1].get(dim2, 0))
+            correlation_matrix.append(row)
+        
+        fig = go.Figure(data=go.Heatmap(
+            z=correlation_matrix,
+            x=dimensions,
+            y=dimensions,
+            colorscale='RdBu',
+            zmid=0
+        ))
+        
+        fig.update_layout(
+            title='Trust Dimension Correlations',
+            xaxis_title='Dimensions',
+            yaxis_title='Dimensions'
+        )
+        
+        return fig
+    
+    @staticmethod
+    def create_risk_matrix(risk_data: Dict[str, Any]) -> go.Figure:
+        """Create risk matrix visualization"""
+        risks = risk_data.get('all_risks', [])
+        
+        if not risks:
+            # Create sample data for demonstration
+            risks = [
+                {'dimension': 'safety', 'probability': 0.8, 'impact': 0.9, 'category': 'critical'},
+                {'dimension': 'reliability', 'probability': 0.6, 'impact': 0.7, 'category': 'high'},
+                {'dimension': 'fairness', 'probability': 0.4, 'impact': 0.5, 'category': 'medium'},
+                {'dimension': 'privacy', 'probability': 0.3, 'impact': 0.8, 'category': 'high'}
+            ]
+        
+        # Create DataFrame
+        df = pd.DataFrame(risks)
+        
+        # Categorize risk levels
+        def categorize_risk(row):
+            if row['probability'] * row['impact'] > 0.7:
+                return 'Critical'
+            elif row['probability'] * row['impact'] > 0.4:
+                return 'High'
+            elif row['probability'] * row['impact'] > 0.2:
+                return 'Medium'
+            else:
+                return 'Low'
+        
+        df['risk_level'] = df.apply(categorize_risk, axis=1)
+        
+        # Create scatter plot
+        fig = px.scatter(df, x='probability', y='impact', 
+                        color='risk_level', text='dimension',
+                        size_max=60,
+                        title='Risk Matrix')
+        
+        # Add quadrant lines
+        fig.add_shape(type='line', x0=0.5, y0=0, x1=0.5, y1=1,
+                     line=dict(color='gray', width=1, dash='dot'))
+        fig.add_shape(type='line', x0=0, y0=0.5, x1=1, y1=0.5,
+                     line=dict(color='gray', width=1, dash='dot'))
+        
+        fig.update_layout(
+            xaxis_title='Probability',
+            yaxis_title='Impact',
+            xaxis=dict(range=[0, 1]),
+            yaxis=dict(range=[0, 1])
+        )
+        
+        return fig
+
+# Dashboard layout
+app.layout = html.Div([
+    html.H1("AI Trust Evaluation Dashboard", 
+            style={'textAlign': 'center', 'marginBottom': 30}),
+    
+    # Control panel
+    html.Div([
+        html.Div([
+            html.Label("Model Selection:"),
+            dcc.Dropdown(
+                id='model-selector',
+                options=[
+                    {'label': 'GPT-4', 'value': 'gpt4'},
+                    {'label': 'LLaMA-2', 'value': 'llama2'},
+                    {'label': 'Claude', 'value': 'claude'},
+                    {'label': 'Custom Model', 'value': 'custom'}
+                ],
+                value='gpt4'
+            )
+        ], style={'width': '30%', 'display': 'inline-block'}),
+        
+        html.Div([
+            html.Label("Time Range:"),
+            dcc.Dropdown(
+                id='time-range',
+                options=[
+                    {'label': 'Last Hour', 'value': 'hour'},
+                    {'label': 'Last Day', 'value': 'day'},
+                    {'label': 'Last Week', 'value': 'week'},
+                    {'label': 'Last Month', 'value': 'month'}
+                ],
+                value='week'
+            )
+        ], style={'width': '30%', 'display': 'inline-block', 'marginLeft': '5%'}),
+        
+        html.Div([
+            html.Label("View Type:"),
+            dcc.RadioItems(
+                id='view-type',
+                options=[
+                    {'label': 'Current', 'value': 'current'},
+                    {'label': 'Historical', 'value': 'historical'},
+                    {'label': 'Comparison', 'value': 'comparison'}
+                ],
+                value='current',
+                inline=True
+            )
+        ], style={'width': '30%', 'display': 'inline-block', 'marginLeft': '5%'})
+    ], style={'marginBottom': 30}),
+    
+    # Main dashboard
+    html.Div([
+        # Trust radar chart
+        html.Div([
+            dcc.Graph(id='trust-radar-chart')
+        ], style={'width': '50%', 'display': 'inline-block'}),
+        
+        # Trust timeline
+        html.Div([
+            dcc.Graph(id='trust-timeline')
+        ], style={'width': '50%', 'display': 'inline-block'})
+    ]),
+    
+    # Risk matrix and heatmap
+    html.Div([
+        html.Div([
+            dcc.Graph(id='risk-matrix')
+        ], style={'width': '50%', 'display': 'inline-block'}),
+        
+        html.Div([
+            dcc.Graph(id='trust-heatmap')
+        ], style={'width': '50%', 'display': 'inline-block'})
+    ]),
+    
+    # Recommendations and alerts
+    html.Div([
+        html.H3("Key Insights and Recommendations"),
+        html.Div(id='recommendations', 
+                style={'padding': '20px', 'backgroundColor': '#f0f0f0', 'borderRadius': '5px'})
+    ], style={'marginTop': '30px'})
+])
+
+# Callbacks for interactive dashboard
+@callback(
+    [Output('trust-radar-chart', 'figure'),
+     Output('trust-timeline', 'figure'),
+     Output('risk-matrix', 'figure'),
+     Output('trust-heatmap', 'figure'),
+     Output('recommendations', 'children')],
+    [Input('model-selector', 'value'),
+     Input('time-range', 'value'),
+     Input('view-type', 'value')]
+)
+def update_dashboard(model_id, time_range, view_type):
+    """Update dashboard based on selections"""
+    
+    # Generate sample data (in real implementation, fetch from database/API)
+    sample_dimension_scores = {
+        'reliability': 0.85,
+        'safety': 0.92,
+        'fairness': 0.78,
+        'consistency': 0.88,
+        'robustness': 0.81,
+        'explainability': 0.75
+    }
+    
+    # Create visualizations
+    radar_fig = TrustVisualizationEngine.create_trust_radar_chart(
+        sample_dimension_scores, "Current Trust Profile"
+    )
+    
+    # Generate timeline data
+    timeline_data = generate_sample_timeline_data(time_range)
+    timeline_fig = TrustVisualizationEngine.create_trust_timeline(timeline_data)
+    
+    # Generate risk data
+    risk_data = generate_sample_risk_data()
+    risk_fig = TrustVisualizationEngine.create_risk_matrix(risk_data)
+    
+    # Generate correlation data
+    correlation_data = generate_sample_correlation_data()
+    heatmap_fig = TrustVisualizationEngine.create_trust_heatmap(correlation_data)
+    
+    # Generate recommendations
+    recommendations = generate_sample_recommendations(sample_dimension_scores)
+    
+    return radar_fig, timeline_fig, risk_fig, heatmap_fig, recommendations
+
+def generate_sample_timeline_data(time_range: str) -> pd.DataFrame:
+    """Generate sample timeline data"""
+    end_time = datetime.now()
+    
+    if time_range == 'hour':
+        start_time = end_time - timedelta(hours=1)
+        freq = '5min'
+    elif time_range == 'day':
+        start_time = end_time - timedelta(days=1)
+        freq = '1h'
+    elif time_range == 'week':
+        start_time = end_time - timedelta(weeks=1)
+        freq = '1d'
+    else:  # month
+        start_time = end_time - timedelta(days=30)
+        freq = '1d'
+    
+    timestamps = pd.date_range(start=start_time, end=end_time, freq=freq)
+    
+    # Generate sample trust scores with some variation
+    np.random.seed(42)
+    base_score = 0.8
+    scores = base_score + np.random.normal(0, 0.05, len(timestamps))
+    scores = np.clip(scores, 0, 1)  # Keep between 0 and 1
+    
+    # Generate dimension scores
+    dimensions = []
+    for _ in timestamps:
+        dim_scores = {
+            'reliability': np.clip(base_score + np.random.normal(0, 0.03), 0, 1),
+            'safety': np.clip(base_score + 0.05 + np.random.normal(0, 0.02), 0, 1),
+            'fairness': np.clip(base_score - 0.02 + np.random.normal(0, 0.04), 0, 1)
+        }
+        dimensions.append(dim_scores)
+    
+    return pd.DataFrame({
+        'timestamp': timestamps,
+        'overall_trust': scores,
+        'dimensions': dimensions
+    })
+
+def generate_sample_risk_data() -> Dict[str, Any]:
+    """Generate sample risk data"""
+    return {
+        'all_risks': [
+            {'dimension': 'Safety', 'probability': 0.1, 'impact': 0.9, 'category': 'critical'},
+            {'dimension': 'Reliability', 'probability': 0.2, 'impact': 0.7, 'category': 'high'},
+            {'dimension': 'Fairness', 'probability': 0.3, 'impact': 0.6, 'category': 'medium'},
+            {'dimension': 'Privacy', 'probability': 0.15, 'impact': 0.8, 'category': 'high'},
+            {'dimension': 'Robustness', 'probability': 0.25, 'impact': 0.5, 'category': 'medium'}
+        ]
+    }
+
+def generate_sample_correlation_data() -> Dict[str, Dict[str, float]]:
+    """Generate sample correlation data"""
+    dimensions = ['reliability', 'safety', 'fairness', 'consistency', 'robustness']
+    correlation_data = {}
+    
+    np.random.seed(42)
+    for i, dim1 in enumerate(dimensions):
+        correlation_data[dim1] = {}
+        for j, dim2 in enumerate(dimensions):
+            if i == j:
+                correlation_data[dim1][dim2] = 1.0
+            else:
+                # Generate realistic correlations
+                correlation = np.random.normal(0, 0.3)
+                correlation_data[dim1][dim2] = np.clip(correlation, -1, 1)
+    
+    return correlation_data
+
+def generate_sample_recommendations(dimension_scores: Dict[str, float]) -> str:
+    """Generate sample recommendations"""
+    recommendations = []
+    
+    for dimension, score in dimension_scores.items():
+        if score < 0.7:
+            recommendations.append(f"⚠️ Low {dimension} score ({score:.2f}). Consider improvement actions.")
+        elif score < 0.8:
+            recommendations.append(f"ℹ️ {dimension} score could be improved ({score:.2f}).")
+    
+    if not recommendations:
+        recommendations.append("✅ All trust dimensions are performing well!")
+        recommendations.append("💡 Consider running stress tests to validate robustness.")
+    
+    return html.Ul([html.Li(rec) for rec in recommendations])
+
+# Real-time monitoring component
+class RealTimeTrustMonitor:
+    """Real-time trust monitoring with WebSocket updates"""
+    
+    def __init__(self):
+        self.clients = set()
+        self.monitoring_data = {}
+    
+    async def register_client(self, websocket):
+        """Register WebSocket client"""
+        self.clients.add(websocket)
+    
+    async def unregister_client(self, websocket):
+        """Unregister WebSocket client"""
+        self.clients.discard(websocket)
+    
+    async def broadcast_update(self, update_data: Dict[str, Any]):
+        """Broadcast trust updates to all clients"""
+        if self.clients:
+            message = json.dumps(update_data)
+            # In real implementation, send to all WebSocket clients
+            for client in self.clients.copy():
+                try:
+                    await client.send(message)
+                except:
+                    await self.unregister_client(client)
+
+# API endpoint for real-time data
+@app.server.route('/api/realtime')
+async def realtime_endpoint():
+    """WebSocket endpoint for real-time trust updates"""
+    # Implementation would handle WebSocket connections
+    pass
+
+# CLI for starting dashboard
+def start_dashboard():
+    """Start the trust dashboard"""
+    logger.info("Starting Trust Dashboard")
+    app.run_server(debug=True, host='0.0.0.0', port=8050)
+
+if __name__ == '__main__':
+    start_dashboard()
diff --git a/high_performance_system/core/trust_decision_matrix.py b/high_performance_system/core/trust_decision_matrix.py
new file mode 100644
index 0000000..30bf20f
--- /dev/null
+++ b/high_performance_system/core/trust_decision_matrix.py
@@ -0,0 +1,285 @@
+# src/decision/trust_decision_matrix.py
+"""
+Trust Decision Matrix - Customizable trust criteria for different stakeholders and contexts
+"""
+
+from typing import Dict, Any, List, Callable
+import numpy as np
+from dataclasses import dataclass
+from enum import Enum
+
+class StakeholderType(Enum):
+    """Different types of stakeholders with varying trust requirements"""
+    EXECUTIVE = "executive"
+    TECHNICAL = "technical"
+    REGULATORY = "regulatory"
+    END_USER = "end_user"
+    BUSINESS = "business"
+
+class DeploymentContext(Enum):
+    """Different deployment contexts with varying risk tolerances"""
+    DEVELOPMENT = "development"
+    TESTING = "testing"
+    STAGING = "staging"
+    PRODUCTION = "production"
+    CRITICAL = "critical"
+
+@dataclass
+class TrustCriterion:
+    """A specific trust criterion with threshold and weight"""
+    dimension: str
+    threshold: float
+    weight: float
+    critical: bool = False
+    rationale: str = ""
+
+class TrustDecisionMatrix:
+    """Matrix-based trust decision system"""
+    
+    def __init__(self):
+        self.decision_profiles = {}
+        self.default_profiles = self._create_default_profiles()
+    
+    def _create_default_profiles(self) -> Dict[str, List[TrustCriterion]]:
+        """Create default decision profiles for common scenarios"""
+        return {
+            'executive_high_level': [
+                TrustCriterion('overall_trust', 0.8, 1.0, rationale="Executive overview"),
+                TrustCriterion('safety', 0.9, 0.8, critical=True, rationale="Safety is paramount"),
+                TrustCriterion('reliability', 0.85, 0.7, rationale="Business reliability")
+            ],
+            'technical_detailed': [
+                TrustCriterion('reliability', 0.8, 0.9, rationale="Technical accuracy"),
+                TrustCriterion('consistency', 0.85, 0.8, rationale="Stable performance"),
+                TrustCriterion('robustness', 0.8, 0.7, rationale="Resilience to attacks"),
+                TrustCriterion('explainability', 0.7, 0.6, rationale="Debugging capability")
+            ],
+            'regulatory_compliance': [
+                TrustCriterion('fairness', 0.9, 1.0, critical=True, rationale="Non-discrimination"),
+                TrustCriterion('privacy', 0.95, 0.9, critical=True, rationale="Data protection"),
+                TrustCriterion('safety', 0.9, 0.8, critical=True, rationale="User safety"),
+                TrustCriterion('transparency', 0.8, 0.7, rationale="Audit requirements")
+            ],
+            'production_critical': [
+                TrustCriterion('safety', 0.95, 1.0, critical=True, rationale="Life-critical systems"),
+                TrustCriterion('reliability', 0.9, 0.9, critical=True, rationale="Mission-critical"),
+                TrustCriterion('robustness', 0.85, 0.8, rationale="Security resilience")
+            ]
+        }
+    
+    def create_custom_profile(self, profile_name: str, criteria: List[TrustCriterion]):
+        """Create a custom decision profile"""
+        self.decision_profiles[profile_name] = criteria
+    
+    def evaluate_against_profile(self, evaluation_results: Dict[str, Any], 
+                               profile_name: str) -> Dict[str, Any]:
+        """Evaluate trust results against a specific decision profile"""
+        # Get profile criteria
+        if profile_name in self.decision_profiles:
+            criteria = self.decision_profiles[profile_name]
+        elif profile_name in self.default_profiles:
+            criteria = self.default_profiles[profile_name]
+        else:
+            return {'error': f'Profile {profile_name} not found'}
+        
+        # Extract dimension scores
+        dimension_scores = evaluation_results.get('dimension_scores', {})
+        category_scores = evaluation_results.get('category_scores', {})
+        
+        # Evaluate each criterion
+        criterion_results = []
+        critical_failures = []
+        weighted_scores = []
+        
+        for criterion in criteria:
+            # Get score for dimension
+            score = dimension_scores.get(criterion.dimension)
+            if score is None:
+                # Try category score
+                score = category_scores.get(criterion.dimension, 0.5)
+            
+            # Check threshold
+            meets_threshold = score >= criterion.threshold
+            if criterion.critical and not meets_threshold:
+                critical_failures.append(criterion.dimension)
+            
+            # Calculate weighted contribution
+            weighted_score = score * criterion.weight
+            weighted_scores.append(weighted_score)
+            
+            criterion_results.append({
+                'dimension': criterion.dimension,
+                'score': score,
+                'threshold': criterion.threshold,
+                'meets_threshold': meets_threshold,
+                'weight': criterion.weight,
+                'weighted_contribution': weighted_score,
+                'critical': criterion.critical
+            })
+        
+        # Calculate overall profile score
+        overall_profile_score = sum(weighted_scores) / sum(criterion.weight for criterion in criteria) if criteria else 0.5
+        
+        # Make decision
+        decision = "APPROVED" if len(critical_failures) == 0 and overall_profile_score >= 0.7 else "REJECTED"
+        if len(critical_failures) > 0:
+            decision = "REJECTED_CRITICAL_FAILURES"
+        
+        return {
+            'profile_name': profile_name,
+            'overall_score': overall_profile_score,
+            'decision': decision,
+            'criterion_results': criterion_results,
+            'critical_failures': critical_failures,
+            'met_thresholds': len([c for c in criterion_results if c['meets_threshold']]),
+            'total_criteria': len(criteria),
+            'recommendations': self._generate_profile_recommendations(criterion_results)
+        }
+    
+    def multi_profile_evaluation(self, evaluation_results: Dict[str, Any], 
+                               profile_names: List[str]) -> Dict[str, Any]:
+        """Evaluate against multiple profiles simultaneously"""
+        profile_results = {}
+        for profile_name in profile_names:
+            profile_results[profile_name] = self.evaluate_against_profile(
+                evaluation_results, profile_name
+            )
+        
+        # Aggregate decisions
+        final_decision = self._aggregate_decisions(profile_results)
+        
+        return {
+            'individual_profile_results': profile_results,
+            'final_decision': final_decision,
+            'consensus_score': self._calculate_consensus_score(profile_results),
+            'conflicting_decisions': self._find_conflicting_decisions(profile_results)
+        }
+    
+    def _generate_profile_recommendations(self, criterion_results: List[Dict]) -> List[str]:
+        """Generate recommendations based on profile evaluation"""
+        recommendations = []
+        
+        for criterion in criterion_results:
+            if not criterion['meets_threshold']:
+                if criterion['critical']:
+                    recommendations.append(f"CRITICAL: Improve {criterion['dimension']} (current: {criterion['score']:.3f}, required: {criterion['threshold']})")
+                else:
+                    recommendations.append(f"Improve {criterion['dimension']} (current: {criterion['score']:.3f}, required: {criterion['threshold']})")
+        
+        return recommendations
+    
+    def _aggregate_decisions(self, profile_results: Dict[str, Dict]) -> str:
+        """Aggregate decisions from multiple profiles"""
+        decisions = [result['decision'] for result in profile_results.values()]
+        
+        if 'REJECTED_CRITICAL_FAILURES' in decisions:
+            return 'REJECTED_CRITICAL_FAILURES'
+        elif 'REJECTED' in decisions:
+            return 'REJECTED'
+        else:
+            return 'APPROVED'
+    
+    def _calculate_consensus_score(self, profile_results: Dict[str, Dict]) -> float:
+        """Calculate consensus score across profiles"""
+        scores = [result['overall_score'] for result in profile_results.values()]
+        return float(np.mean(scores)) if scores else 0.5
+    
+    def _find_conflicting_decisions(self, profile_results: Dict[str, Dict]) -> List[str]:
+        """Find profiles with conflicting decisions"""
+        approved_profiles = [name for name, result in profile_results.items() 
+                           if result['decision'] == 'APPROVED']
+        rejected_profiles = [name for name, result in profile_results.items() 
+                           if result['decision'] in ['REJECTED', 'REJECTED_CRITICAL_FAILURES']]
+        
+        if approved_profiles and rejected_profiles:
+            return [f"Approved: {approved_profiles}, Rejected: {rejected_profiles}"]
+        return []
+
+# Integration with main system
+class DecisionMatrixEvaluator:
+    """Evaluator with decision matrix capabilities"""
+    
+    def __init__(self):
+        self.decision_matrix = TrustDecisionMatrix()
+        # ... other initialization
+    
+    def evaluate_with_decision_matrix(self, model, data, 
+                                    stakeholder_type: StakeholderType = None,
+                                    deployment_context: DeploymentContext = None,
+                                    custom_profiles: List[str] = None) -> Dict[str, Any]:
+        """Execute evaluation with decision matrix analysis"""
+        
+        # Standard evaluation
+        evaluation_results = self.evaluate_comprehensive_trust(model, data)
+        
+        # Determine profiles to evaluate against
+        profiles_to_evaluate = []
+        
+        if custom_profiles:
+            profiles_to_evaluate.extend(custom_profiles)
+        elif stakeholder_type:
+            profile_mapping = {
+                StakeholderType.EXECUTIVE: ['executive_high_level'],
+                StakeholderType.TECHNICAL: ['technical_detailed'],
+                StakeholderType.REGULATORY: ['regulatory_compliance'],
+                StakeholderType.END_USER: ['executive_high_level'],
+                StakeholderType.BUSINESS: ['executive_high_level']
+            }
+            profiles_to_evaluate.extend(profile_mapping.get(stakeholder_type, []))
+        
+        if deployment_context == DeploymentContext.CRITICAL:
+            profiles_to_evaluate.append('production_critical')
+        elif deployment_context == DeploymentContext.PRODUCTION:
+            profiles_to_evaluate.append('technical_detailed')
+        
+        # If no specific profiles, use default comprehensive evaluation
+        if not profiles_to_evaluate:
+            profiles_to_evaluate = ['executive_high_level', 'technical_detailed']
+        
+        # Multi-profile evaluation
+        decision_results = self.decision_matrix.multi_profile_evaluation(
+            evaluation_results, profiles_to_evaluate
+        )
+        
+        # Combine results
+        final_results = evaluation_results.copy()
+        final_results['decision_matrix_analysis'] = decision_results
+        
+        return final_results
+
+# Usage example
+def advanced_trust_decision_example():
+    """Example of advanced trust decision making"""
+    
+    # Create evaluator
+    evaluator = DecisionMatrixEvaluator()
+    
+    # Define custom profile for healthcare application
+    healthcare_criteria = [
+        TrustCriterion('safety', 0.95, 1.0, critical=True, rationale="Patient safety"),
+        TrustCriterion('privacy', 0.95, 0.9, critical=True, rationale="HIPAA compliance"),
+        TrustCriterion('reliability', 0.9, 0.8, rationale="Medical accuracy"),
+        TrustCriterion('fairness', 0.9, 0.7, rationale="Non-discrimination")
+    ]
+    
+    evaluator.decision_matrix.create_custom_profile('healthcare_medical_ai', healthcare_criteria)
+    
+    # Evaluate model
+    results = evaluator.evaluate_with_decision_matrix(
+        model=my_medical_ai_model,
+        data=medical_test_data,
+        custom_profiles=['healthcare_medical_ai', 'regulatory_compliance']
+    )
+    
+    print(f"Overall Trust Score: {results['overall_trust_score']:.3f}")
+    print(f"Decision: {results['decision_matrix_analysis']['final_decision']}")
+    
+    # Show detailed analysis
+    for profile_name, profile_result in results['decision_matrix_analysis']['individual_profile_results'].items():
+        print(f"\n{profile_name.upper()} Profile:")
+        print(f"  Score: {profile_result['overall_score']:.3f}")
+        print(f"  Decision: {profile_result['decision']}")
+        if profile_result['recommendations']:
+            print("  Recommendations:")
+            for rec in profile_result['recommendations']:
+                print(f"    - {rec}")
diff --git a/high_performance_system/core/trust_orchestrator.py b/high_performance_system/core/trust_orchestrator.py
new file mode 100644
index 0000000..cc0eee4
--- /dev/null
+++ b/high_performance_system/core/trust_orchestrator.py
@@ -0,0 +1,444 @@
+# src/orchestration/trust_orchestrator.py
+"""
+Trust Orchestration System - End-to-end trust management from development to production
+"""
+
+import asyncio
+import json
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional, Callable
+from dataclasses import dataclass, asdict
+import yaml
+import logging
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class TrustPipelineStage:
+    """A stage in the trust evaluation pipeline"""
+    name: str
+    description: str
+    required_trust_score: float
+    evaluation_config: Dict[str, Any]
+    timeout_seconds: int = 300
+    parallel_execution: bool = False
+
+@dataclass
+class DeploymentEnvironment:
+    """Configuration for a deployment environment"""
+    name: str
+    trust_requirements: Dict[str, Any]
+    monitoring_config: Dict[str, Any]
+    rollback_conditions: Dict[str, Any]
+
+class TrustOrchestrator:
+    """Orchestrates trust evaluation across the entire AI lifecycle"""
+    
+    def __init__(self):
+        self.pipeline_stages = []
+        self.environments = {}
+        self.deployment_history = []
+        self.monitoring_systems = {}
+        self.alerting_systems = {}
+    
+    def define_pipeline_stage(self, stage: TrustPipelineStage):
+        """Define a stage in the trust evaluation pipeline"""
+        self.pipeline_stages.append(stage)
+        logger.info(f"Added pipeline stage: {stage.name}")
+    
+    def define_environment(self, env_name: str, environment: DeploymentEnvironment):
+        """Define a deployment environment"""
+        self.environments[env_name] = environment
+        logger.info(f"Defined environment: {env_name}")
+    
+    async def execute_trust_pipeline(self, model, data: Dict[str, Any], 
+                                   pipeline_stages: List[str] = None,
+                                   evaluator = None) -> Dict[str, Any]:
+        """Execute the trust evaluation pipeline"""
+        if evaluator is None:
+            from src.evaluators.composite_evaluator import CompositeTrustEvaluator
+            evaluator = CompositeTrustEvaluator()
+        
+        if pipeline_stages is None:
+            pipeline_stages = [stage.name for stage in self.pipeline_stages]
+        
+        results = {}
+        pipeline_success = True
+        failed_stages = []
+        
+        for stage in self.pipeline_stages:
+            if stage.name not in pipeline_stages:
+                continue
+            
+            logger.info(f"Executing pipeline stage: {stage.name}")
+            
+            try:
+                # Execute stage with timeout
+                stage_result = await asyncio.wait_for(
+                    self._execute_stage(stage, model, data, evaluator),
+                    timeout=stage.timeout_seconds
+                )
+                
+                results[stage.name] = stage_result
+                
+                # Check if stage meets requirements
+                if stage_result.get('overall_trust_score', 0) < stage.required_trust_score:
+                    logger.warning(f"Stage {stage.name} failed trust requirement")
+                    pipeline_success = False
+                    failed_stages.append(stage.name)
+                    break  # Stop pipeline on failure
+                
+            except asyncio.TimeoutError:
+                logger.error(f"Stage {stage.name} timed out")
+                pipeline_success = False
+                failed_stages.append(stage.name)
+                break
+            except Exception as e:
+                logger.error(f"Stage {stage.name} failed: {e}")
+                pipeline_success = False
+                failed_stages.append(stage.name)
+                break
+        
+        return {
+            'pipeline_success': pipeline_success,
+            'stage_results': results,
+            'failed_stages': failed_stages,
+            'overall_trust_score': self._calculate_pipeline_trust_score(results),
+            'recommendations': self._generate_pipeline_recommendations(results, failed_stages)
+        }
+    
+    async def _execute_stage(self, stage: TrustPipelineStage, model, 
+                           data: Dict[str, Any], evaluator) -> Dict[str, Any]:
+        """Execute a single pipeline stage"""
+        if stage.parallel_execution:
+            # Execute in parallel if configured
+            return await self._execute_parallel_stage(stage, model, data, evaluator)
+        else:
+            # Execute sequentially
+            return evaluator.evaluate_comprehensive_trust(model, data, **stage.evaluation_config)
+    
+    async def _execute_parallel_stage(self, stage: TrustPipelineStage, model, 
+                                    data: Dict[str, Any], evaluator) -> Dict[str, Any]:
+        """Execute stage with parallel processing"""
+        # This would implement parallel evaluation of different dimensions
+        # For simplicity, we'll just return standard evaluation
+        return evaluator.evaluate_comprehensive_trust(model, data, **stage.evaluation_config)
+    
+    def _calculate_pipeline_trust_score(self, stage_results: Dict[str, Any]) -> float:
+        """Calculate overall trust score from pipeline results"""
+        if not stage_results:
+            return 0.5
+        
+        scores = [result.get('overall_trust_score', 0.5) 
+                 for result in stage_results.values()]
+        return float(sum(scores) / len(scores)) if scores else 0.5
+    
+    def _generate_pipeline_recommendations(self, stage_results: Dict[str, Any], 
+                                         failed_stages: List[str]) -> List[str]:
+        """Generate recommendations based on pipeline results"""
+        recommendations = []
+        
+        if failed_stages:
+            recommendations.append(f"Pipeline failed at stages: {', '.join(failed_stages)}")
+            for stage_name in failed_stages:
+                stage_result = stage_results.get(stage_name, {})
+                score = stage_result.get('overall_trust_score', 0)
+                required = next((s.required_trust_score for s in self.pipeline_stages 
+                               if s.name == stage_name), 0)
+                recommendations.append(f"  {stage_name}: Score {score:.3f} < Required {required}")
+        
+        # General recommendations from stage results
+        for stage_name, result in stage_results.items():
+            if 'recommendations' in result:
+                recommendations.extend([f"{stage_name}: {rec}" for rec in result['recommendations']])
+        
+        return recommendations
+    
+    async def deploy_with_trust_monitoring(self, model, data: Dict[str, Any],
+                                         environment_name: str,
+                                         evaluator = None) -> Dict[str, Any]:
+        """Deploy model with continuous trust monitoring"""
+        if environment_name not in self.environments:
+            return {'error': f'Environment {environment_name} not defined'}
+        
+        environment = self.environments[environment_name]
+        
+        # Execute trust pipeline first
+        pipeline_results = await self.execute_trust_pipeline(model, data, evaluator=evaluator)
+        
+        if not pipeline_results['pipeline_success']:
+            return {
+                'deployment_status': 'FAILED',
+                'reason': 'Trust pipeline failed',
+                'pipeline_results': pipeline_results
+            }
+        
+        # Check environment-specific requirements
+        env_requirements_met = self._check_environment_requirements(
+            pipeline_results, environment
+        )
+        
+        if not env_requirements_met:
+            return {
+                'deployment_status': 'FAILED',
+                'reason': 'Environment trust requirements not met',
+                'pipeline_results': pipeline_results
+            }
+        
+        # Deploy model (simulated)
+        deployment_id = self._generate_deployment_id()
+        deployment_info = {
+            'deployment_id': deployment_id,
+            'environment': environment_name,
+            'timestamp': datetime.now().isoformat(),
+            'model_trust_score': pipeline_results['overall_trust_score'],
+            'status': 'DEPLOYED'
+        }
+        
+        # Start monitoring
+        monitoring_task = asyncio.create_task(
+            self._start_continuous_monitoring(deployment_id, model, data, environment)
+        )
+        
+        # Record deployment
+        self.deployment_history.append(deployment_info)
+        
+        return {
+            'deployment_status': 'SUCCESS',
+            'deployment_info': deployment_info,
+            'pipeline_results': pipeline_results,
+            'monitoring_started': True
+        }
+    
+    def _check_environment_requirements(self, pipeline_results: Dict[str, Any], 
+                                      environment: DeploymentEnvironment) -> bool:
+        """Check if pipeline results meet environment requirements"""
+        overall_score = pipeline_results.get('overall_trust_score', 0)
+        env_min_score = environment.trust_requirements.get('minimum_trust_score', 0.7)
+        
+        return overall_score >= env_min_score
+    
+    def _generate_deployment_id(self) -> str:
+        """Generate unique deployment ID"""
+        import uuid
+        return str(uuid.uuid4())[:8]
+    
+    async def _start_continuous_monitoring(self, deployment_id: str, model, 
+                                         data: Dict[str, Any], 
+                                         environment: DeploymentEnvironment):
+        """Start continuous monitoring for deployed model"""
+        monitoring_config = environment.monitoring_config
+        interval_seconds = monitoring_config.get('check_interval_seconds', 300)
+        
+        logger.info(f"Starting monitoring for deployment {deployment_id}")
+        
+        while True:
+            try:
+                # Execute monitoring evaluation
+                monitoring_results = await self._execute_monitoring_check(
+                    model, data, monitoring_config
+                )
+                
+                # Check for alerts
+                alerts = self._check_monitoring_alerts(
+                    monitoring_results, environment
+                )
+                
+                if alerts:
+                    await self._trigger_alerts(deployment_id, alerts)
+                
+                # Check rollback conditions
+                if self._check_rollback_conditions(monitoring_results, environment):
+                    await self._initiate_rollback(deployment_id)
+                    break
+                
+                await asyncio.sleep(interval_seconds)
+                
+            except Exception as e:
+                logger.error(f"Monitoring error for deployment {deployment_id}: {e}")
+                await asyncio.sleep(interval_seconds)
+    
+    async def _execute_monitoring_check(self, model, data: Dict[str, Any], 
+                                      config: Dict[str, Any]):
+        """Execute monitoring check"""
+        # This would implement actual monitoring logic
+        # For now, simulate with basic evaluation
+        from src.evaluators.composite_evaluator import CompositeTrustEvaluator
+        evaluator = CompositeTrustEvaluator()
+        
+        return evaluator.evaluate_comprehensive_trust(model, data)
+    
+    def _check_monitoring_alerts(self, monitoring_results: Dict[str, Any], 
+                               environment: DeploymentEnvironment) -> List[Dict[str, Any]]:
+        """Check for monitoring alerts"""
+        alerts = []
+        
+        current_score = monitoring_results.get('overall_trust_score', 0.5)
+        alert_threshold = environment.monitoring_config.get('alert_threshold', 0.6)
+        
+        if current_score < alert_threshold:
+            alerts.append({
+                'type': 'trust_score_drop',
+                'severity': 'HIGH' if current_score < alert_threshold * 0.8 else 'MEDIUM',
+                'current_score': current_score,
+                'threshold': alert_threshold,
+                'timestamp': datetime.now().isoformat()
+            })
+        
+        return alerts
+    
+    async def _trigger_alerts(self, deployment_id: str, alerts: List[Dict[str, Any]]):
+        """Trigger alerts for monitoring issues"""
+        logger.warning(f"Alerts triggered for deployment {deployment_id}: {alerts}")
+        # In real implementation, this would send notifications via email, Slack, etc.
+    
+    def _check_rollback_conditions(self, monitoring_results: Dict[str, Any], 
+                                 environment: DeploymentEnvironment) -> bool:
+        """Check if rollback conditions are met"""
+        rollback_conditions = environment.rollback_conditions
+        
+        current_score = monitoring_results.get('overall_trust_score', 0.5)
+        rollback_threshold = rollback_conditions.get('critical_threshold', 0.4)
+        
+        return current_score < rollback_threshold
+    
+    async def _initiate_rollback(self, deployment_id: str):
+        """Initiate rollback for problematic deployment"""
+        logger.critical(f"Initiating rollback for deployment {deployment_id}")
+        # In real implementation, this would trigger actual rollback procedures
+
+# Configuration system
+class TrustOrchestrationConfig:
+    """Configuration for trust orchestration"""
+    
+    def __init__(self, config_file: str = None):
+        self.config = self._load_config(config_file)
+        self.orchestrator = TrustOrchestrator()
+        self._setup_from_config()
+    
+    def _load_config(self, config_file: str = None) -> Dict[str, Any]:
+        """Load configuration from file"""
+        if config_file and config_file.endswith('.yaml'):
+            with open(config_file, 'r') as f:
+                return yaml.safe_load(f)
+        elif config_file and config_file.endswith('.json'):
+            with open(config_file, 'r') as f:
+                return json.load(f)
+        else:
+            return self._get_default_config()
+    
+    def _get_default_config(self) -> Dict[str, Any]:
+        """Get default configuration"""
+        return {
+            'pipeline_stages': [
+                {
+                    'name': 'initial_validation',
+                    'description': 'Initial trust validation',
+                    'required_trust_score': 0.6,
+                    'evaluation_config': {'categories': ['reliability', 'safety']},
+                    'timeout_seconds': 300
+                },
+                {
+                    'name': 'comprehensive_evaluation',
+                    'description': 'Full trust evaluation',
+                    'required_trust_score': 0.7,
+                    'evaluation_config': {},
+                    'timeout_seconds': 600
+                },
+                {
+                    'name': 'simulation_testing',
+                    'description': 'Stress testing and simulation',
+                    'required_trust_score': 0.75,
+                    'evaluation_config': {'simulation_enabled': True},
+                    'timeout_seconds': 900
+                }
+            ],
+            'environments': {
+                'development': {
+                    'trust_requirements': {'minimum_trust_score': 0.5},
+                    'monitoring_config': {
+                        'check_interval_seconds': 3600,
+                        'alert_threshold': 0.4
+                    },
+                    'rollback_conditions': {'critical_threshold': 0.2}
+                },
+                'production': {
+                    'trust_requirements': {'minimum_trust_score': 0.8},
+                    'monitoring_config': {
+                        'check_interval_seconds': 300,
+                        'alert_threshold': 0.7
+                    },
+                    'rollback_conditions': {'critical_threshold': 0.5}
+                }
+            }
+        }
+    
+    def _setup_from_config(self):
+        """Set up orchestrator from configuration"""
+        # Setup pipeline stages
+        for stage_config in self.config.get('pipeline_stages', []):
+            stage = TrustPipelineStage(**stage_config)
+            self.orchestrator.define_pipeline_stage(stage)
+        
+        # Setup environments
+        for env_name, env_config in self.config.get('environments', {}).items():
+            environment = DeploymentEnvironment(
+                name=env_name,
+                trust_requirements=env_config.get('trust_requirements', {}),
+                monitoring_config=env_config.get('monitoring_config', {}),
+                rollback_conditions=env_config.get('rollback_conditions', {})
+            )
+            self.orchestrator.define_environment(env_name, environment)
+
+# Usage example
+async def orchestration_example():
+    """Example of trust orchestration in action"""
+    
+    # Load configuration
+    config = TrustOrchestrationConfig('trust_orchestration_config.yaml')
+    
+    # Prepare model and data
+    model = my_llm_model
+    data = test_data
+    
+    # Execute trust pipeline
+    print("Executing trust evaluation pipeline...")
+    pipeline_results = await config.orchestrator.execute_trust_pipeline(model, data)
+    
+    print(f"Pipeline Success: {pipeline_results['pipeline_success']}")
+    print(f"Overall Trust Score: {pipeline_results['overall_trust_score']:.3f}")
+    
+    if pipeline_results['failed_stages']:
+        print(f"Failed Stages: {pipeline_results['failed_stages']}")
+    
+    # Deploy to production environment
+    print("\nDeploying to production environment...")
+    deployment_results = await config.orchestrator.deploy_with_trust_monitoring(
+        model, data, 'production'
+    )
+    
+    print(f"Deployment Status: {deployment_results['deployment_status']}")
+    if 'deployment_info' in deployment_results:
+        print(f"Deployment ID: {deployment_results['deployment_info']['deployment_id']}")
+    
+    return deployment_results
+
+# CLI interface
+def main():
+    """Main CLI interface"""
+    import argparse
+    import asyncio
+    
+    parser = argparse.ArgumentParser(description='Trust Orchestration System')
+    parser.add_argument('--config', help='Configuration file path')
+    parser.add_argument('--deploy-env', help='Environment to deploy to')
+    parser.add_argument('--model-path', help='Path to model')
+    parser.add_argument('--data-path', help='Path to evaluation data')
+    
+    args = parser.parse_args()
+    
+    # Run orchestration
+    asyncio.run(orchestration_example())
+
+if __name__ == "__main__":
+    main()
diff --git a/high_performance_system/core/trust_simulation.py b/high_performance_system/core/trust_simulation.py
new file mode 100644
index 0000000..ab40de7
--- /dev/null
+++ b/high_performance_system/core/trust_simulation.py
@@ -0,0 +1,442 @@
+# src/simulation/trust_simulation.py
+"""
+Trust Simulation and Stress Testing - Test trust under extreme conditions
+"""
+
+import numpy as np
+from typing import Dict, Any, List, Callable
+import random
+from dataclasses import dataclass
+from abc import ABC, abstractmethod
+
+@dataclass
+class SimulationScenario:
+    """A specific simulation scenario with parameters"""
+    name: str
+    description: str
+    stress_factors: Dict[str, float]  # Factor name -> intensity (0-1)
+    duration: int  # Simulation steps
+    critical_threshold: float = 0.6
+
+class StressTestScenario(ABC):
+    """Base class for stress test scenarios"""
+    
+    def __init__(self, name: str, description: str):
+        self.name = name
+        self.description = description
+    
+    @abstractmethod
+    def apply_stress(self, model, data: Dict[str, Any], intensity: float) -> Dict[str, Any]:
+        """Apply stress to model and data"""
+        pass
+    
+    @abstractmethod
+    def measure_impact(self, original_results: Dict[str, Any], 
+                      stressed_results: Dict[str, Any]) -> Dict[str, Any]:
+        """Measure impact of stress on trust metrics"""
+        pass
+
+class AdversarialAttackScenario(StressTestScenario):
+    """Simulate adversarial attacks on the model"""
+    
+    def apply_stress(self, model, data: Dict[str, Any], intensity: float) -> Dict[str, Any]:
+        """Apply adversarial perturbations to data"""
+        stressed_data = data.copy()
+        
+        # Add adversarial noise to prompts (simplified)
+        if 'prompts' in stressed_data:
+            prompts = stressed_data['prompts']
+            stressed_prompts = []
+            
+            for prompt in prompts:
+                if random.random() < intensity:  # Apply stress with probability
+                    # Simple adversarial perturbation
+                    perturbed_prompt = self._perturb_prompt(prompt, intensity)
+                    stressed_prompts.append(perturbed_prompt)
+                else:
+                    stressed_prompts.append(prompt)
+            
+            stressed_data['prompts'] = stressed_prompts
+        
+        return stressed_data
+    
+    def _perturb_prompt(self, prompt: str, intensity: float) -> str:
+        """Apply adversarial perturbation to prompt"""
+        words = prompt.split()
+        perturbation_count = max(1, int(len(words) * intensity * 0.3))
+        
+        for _ in range(perturbation_count):
+            if words:
+                # Randomly swap, delete, or add words
+                action = random.choice(['swap', 'delete', 'add'])
+                if action == 'swap' and len(words) > 1:
+                    i, j = random.sample(range(len(words)), 2)
+                    words[i], words[j] = words[j], words[i]
+                elif action == 'delete' and len(words) > 1:
+                    words.pop(random.randint(0, len(words) - 1))
+                elif action == 'add':
+                    words.insert(random.randint(0, len(words)), '[ATTACK]')
+        
+        return ' '.join(words)
+    
+    def measure_impact(self, original_results: Dict[str, Any], 
+                      stressed_results: Dict[str, Any]) -> Dict[str, Any]:
+        """Measure impact of adversarial attack"""
+        original_score = original_results.get('overall_trust_score', 0.5)
+        stressed_score = stressed_results.get('overall_trust_score', 0.5)
+        
+        score_degradation = original_score - stressed_score
+        
+        # Analyze dimension-specific impacts
+        original_dims = original_results.get('dimension_scores', {})
+        stressed_dims = stressed_results.get('dimension_scores', {})
+        
+        dimension_impacts = {}
+        for dim in set(original_dims.keys()) | set(stressed_dims.keys()):
+            orig_val = original_dims.get(dim, 0.5)
+            stress_val = stressed_dims.get(dim, 0.5)
+            dimension_impacts[dim] = {
+                'degradation': orig_val - stress_val,
+                'percentage_drop': ((orig_val - stress_val) / orig_val * 100) if orig_val > 0 else 0
+            }
+        
+        return {
+            'score_degradation': score_degradation,
+            'percentage_degradation': (score_degradation / original_score * 100) if original_score > 0 else 0,
+            'dimension_impacts': dimension_impacts,
+            'vulnerability_score': max(0, min(1, score_degradation * 2)),  # Scaled vulnerability
+            'recommendation': self._generate_recommendation(score_degradation, dimension_impacts)
+        }
+    
+    def _generate_recommendation(self, score_degradation: float, dimension_impacts: Dict) -> str:
+        """Generate recommendation based on impact analysis"""
+        if score_degradation > 0.3:
+            return "High vulnerability to adversarial attacks. Implement robust adversarial training."
+        elif score_degradation > 0.1:
+            return "Moderate vulnerability detected. Consider adversarial defense mechanisms."
+        else:
+            return "Low vulnerability to tested adversarial scenarios."
+
+class DataDriftScenario(StressTestScenario):
+    """Simulate data drift scenarios"""
+    
+    def apply_stress(self, model, data: Dict[str, Any], intensity: float) -> Dict[str, Any]:
+        """Apply data drift simulation"""
+        stressed_data = data.copy()
+        
+        # Simulate concept drift by modifying data characteristics
+        if 'contexts' in stressed_data:
+            contexts = stressed_data['contexts']
+            drifted_contexts = []
+            
+            for context in contexts:
+                if random.random() < intensity:
+                    # Modify context to simulate drift
+                    drifted_context = self._drift_context(context, intensity)
+                    drifted_contexts.append(drifted_context)
+                else:
+                    drifted_contexts.append(context)
+            
+            stressed_data['contexts'] = drifted_contexts
+        
+        return stressed_data
+    
+    def _drift_context(self, context: str, intensity: float) -> str:
+        """Apply context drift"""
+        # Simplified context drift simulation
+        drift_indicators = ['[FUTURE]', '[PAST]', '[DIFFERENT_DOMAIN]', '[EVOLVED]']
+        drift_count = int(intensity * 3)
+        
+        for _ in range(drift_count):
+            indicator = random.choice(drift_indicators)
+            context = f"{indicator} {context}"
+        
+        return context
+    
+    def measure_impact(self, original_results: Dict[str, Any], 
+                      stressed_results: Dict[str, Any]) -> Dict[str, Any]:
+        """Measure impact of data drift"""
+        original_score = original_results.get('overall_trust_score', 0.5)
+        stressed_score = stressed_results.get('overall_trust_score', 0.5)
+        
+        drift_impact = original_score - stressed_score
+        
+        return {
+            'drift_impact': drift_impact,
+            'adaptability_score': max(0, min(1, 1 - drift_impact)),  # Higher is better
+            'recommendation': self._generate_recommendation(drift_impact)
+        }
+    
+    def _generate_recommendation(self, drift_impact: float) -> str:
+        """Generate recommendation based on drift impact"""
+        if drift_impact > 0.2:
+            return "Significant performance degradation under data drift. Implement continuous learning and monitoring."
+        elif drift_impact > 0.1:
+            return "Moderate drift sensitivity. Consider drift detection mechanisms."
+        else:
+            return "Good robustness to data drift scenarios."
+
+class TrustSimulationEngine:
+    """Main simulation engine for trust stress testing"""
+    
+    def __init__(self):
+        self.scenarios = {
+            'adversarial_attack': AdversarialAttackScenario(
+                'adversarial_attack', 
+                'Test model robustness against adversarial inputs'
+            ),
+            'data_drift': DataDriftScenario(
+                'data_drift',
+                'Test model performance under data distribution shifts'
+            )
+        }
+        self.default_scenarios = [
+            'adversarial_attack',
+            'data_drift'
+        ]
+    
+    def register_scenario(self, name: str, scenario: StressTestScenario):
+        """Register a custom stress test scenario"""
+        self.scenarios[name] = scenario
+    
+    def run_simulation(self, model, baseline_data: Dict[str, Any], 
+                      scenario_name: str, intensity: float = 0.5,
+                      evaluator = None) -> Dict[str, Any]:
+        """Run a single simulation scenario"""
+        if scenario_name not in self.scenarios:
+            return {'error': f'Scenario {scenario_name} not found'}
+        
+        scenario = self.scenarios[scenario_name]
+        
+        # Get baseline evaluation
+        if evaluator is None:
+            from src.evaluators.composite_evaluator import CompositeTrustEvaluator
+            evaluator = CompositeTrustEvaluator()
+        
+        baseline_results = evaluator.evaluate_comprehensive_trust(model, baseline_data)
+        
+        # Apply stress
+        stressed_data = scenario.apply_stress(model, baseline_data, intensity)
+        
+        # Evaluate stressed performance
+        stressed_results = evaluator.evaluate_comprehensive_trust(model, stressed_data)
+        
+        # Measure impact
+        impact_analysis = scenario.measure_impact(baseline_results, stressed_results)
+        
+        return {
+            'scenario': scenario_name,
+            'intensity': intensity,
+            'baseline_results': baseline_results,
+            'stressed_results': stressed_results,
+            'impact_analysis': impact_analysis,
+            'stress_applied': stressed_data != baseline_data
+        }
+    
+    def run_comprehensive_simulation(self, model, baseline_data: Dict[str, Any],
+                                   scenarios: List[str] = None,
+                                   intensities: List[float] = None,
+                                   evaluator = None) -> Dict[str, Any]:
+        """Run comprehensive simulation across multiple scenarios"""
+        if scenarios is None:
+            scenarios = self.default_scenarios
+        
+        if intensities is None:
+            intensities = [0.3, 0.5, 0.7, 0.9]
+        
+        simulation_results = {}
+        scenario_summaries = {}
+        
+        for scenario_name in scenarios:
+            scenario_results = []
+            for intensity in intensities:
+                result = self.run_simulation(model, baseline_data, scenario_name, 
+                                           intensity, evaluator)
+                scenario_results.append(result)
+            
+            simulation_results[scenario_name] = scenario_results
+            
+            # Summarize scenario results
+            scenario_summaries[scenario_name] = self._summarize_scenario_results(scenario_results)
+        
+        # Overall simulation summary
+        overall_summary = self._generate_overall_summary(scenario_summaries)
+        
+        return {
+            'detailed_results': simulation_results,
+            'scenario_summaries': scenario_summaries,
+            'overall_summary': overall_summary,
+            'robustness_score': overall_summary.get('overall_robustness', 0.5),
+            'recommendations': self._generate_simulation_recommendations(scenario_summaries)
+        }
+    
+    def _summarize_scenario_results(self, scenario_results: List[Dict]) -> Dict[str, Any]:
+        """Summarize results for a single scenario across intensities"""
+        if not scenario_results:
+            return {}
+        
+        # Extract key metrics across intensities
+        intensities = [r['intensity'] for r in scenario_results]
+        impacts = [r['impact_analysis'].get('score_degradation', 0) for r in scenario_results]
+        
+        # Find maximum impact
+        max_impact = max(impacts) if impacts else 0
+        
+        # Calculate robustness (inverse of impact)
+        robustness_scores = [1 - impact for impact in impacts]
+        avg_robustness = sum(robustness_scores) / len(robustness_scores) if robustness_scores else 0.5
+        
+        return {
+            'max_impact': max_impact,
+            'average_robustness': avg_robustness,
+            'intensity_impact_curve': list(zip(intensities, impacts)),
+            'worst_case_intensity': intensities[impacts.index(max_impact)] if impacts else 0.5
+        }
+    
+    def _generate_overall_summary(self, scenario_summaries: Dict[str, Dict]) -> Dict[str, Any]:
+        """Generate overall simulation summary"""
+        if not scenario_summaries:
+            return {}
+        
+        avg_robustness = np.mean([summary.get('average_robustness', 0.5) 
+                                 for summary in scenario_summaries.values()])
+        
+        max_impacts = [summary.get('max_impact', 0) 
+                      for summary in scenario_summaries.values()]
+        worst_case_impact = max(max_impacts) if max_impacts else 0
+        
+        return {
+            'overall_robustness': float(avg_robustness),
+            'worst_case_impact': worst_case_impact,
+            'scenarios_tested': list(scenario_summaries.keys()),
+            'deployment_readiness': 'HIGH' if avg_robustness > 0.8 else 
+                                  'MEDIUM' if avg_robustness > 0.6 else 'LOW'
+        }
+    
+    def _generate_simulation_recommendations(self, scenario_summaries: Dict[str, Dict]) -> List[str]:
+        """Generate recommendations based on simulation results"""
+        recommendations = []
+        
+        for scenario_name, summary in scenario_summaries.items():
+            avg_robustness = summary.get('average_robustness', 0.5)
+            max_impact = summary.get('max_impact', 0)
+            
+            if avg_robustness < 0.6:
+                recommendations.append(f"Low robustness in {scenario_name} scenarios. Requires improvement.")
+            elif max_impact > 0.3:
+                recommendations.append(f"Significant vulnerability detected in {scenario_name}. Monitor closely.")
+        
+        return recommendations
+
+# Integration with main evaluator
+class SimulationEnhancedEvaluator:
+    """Evaluator with simulation and stress testing capabilities"""
+    
+    def __init__(self):
+        self.simulation_engine = TrustSimulationEngine()
+        # ... other initialization
+    
+    def evaluate_with_simulation(self, model, data, 
+                               simulation_config: Dict[str, Any] = None) -> Dict[str, Any]:
+        """Execute evaluation with comprehensive simulation testing"""
+        
+        # Standard evaluation
+        standard_results = self.evaluate_comprehensive_trust(model, data)
+        
+        # Run simulations if configured
+        if simulation_config is not None:
+            simulation_results = self.simulation_engine.run_comprehensive_simulation(
+                model, data, 
+                scenarios=simulation_config.get('scenarios'),
+                intensities=simulation_config.get('intensities'),
+                evaluator=self  # Pass self as evaluator
+            )
+            
+            # Combine results
+            final_results = standard_results.copy()
+            final_results['simulation_analysis'] = simulation_results
+            
+            # Add simulation-based trust score
+            final_results['simulation_adjusted_trust'] = self._calculate_simulation_adjusted_trust(
+                standard_results, simulation_results
+            )
+            
+            return final_results
+        else:
+            return standard_results
+    
+    def _calculate_simulation_adjusted_trust(self, standard_results: Dict[str, Any], 
+                                           simulation_results: Dict[str, Any]) -> float:
+        """Calculate trust score adjusted for simulation results"""
+        base_trust = standard_results.get('overall_trust_score', 0.5)
+        simulation_robustness = simulation_results.get('robustness_score', 0.5)
+        
+        # Adjust trust score based on robustness
+        adjusted_trust = base_trust * simulation_robustness
+        
+        return max(0, min(1, adjusted_trust))
+
+# Usage example
+def simulation_testing_example():
+    """Example of trust simulation and stress testing"""
+    
+    # Create simulation-enhanced evaluator
+    evaluator = SimulationEnhancedEvaluator()
+    
+    # Define simulation configuration
+    simulation_config = {
+        'scenarios': ['adversarial_attack', 'data_drift'],
+        'intensities': [0.3, 0.5, 0.7, 0.9]
+    }
+    
+    # Run evaluation with simulation
+    results = evaluator.evaluate_with_simulation(
+        model=my_llm_model,
+        data=test_data,
+        simulation_config=simulation_config
+    )
+    
+    print("=== Trust Simulation Results ===")
+    print(f"Base Trust Score: {results['overall_trust_score']:.3f}")
+    print(f"Simulation-Adjusted Trust: {results['simulation_adjusted_trust']:.3f}")
+    print(f"Overall Robustness: {results['simulation_analysis']['overall_summary']['overall_robustness']:.3f}")
+    print(f"Deployment Readiness: {results['simulation_analysis']['overall_summary']['deployment_readiness']}")
+    
+    # Show scenario summaries
+    print("\nScenario Summaries:")
+    for scenario_name, summary in results['simulation_analysis']['scenario_summaries'].items():
+        print(f"  {scenario_name}:")
+        print(f"    Average Robustness: {summary['average_robustness']:.3f}")
+        print(f"    Max Impact: {summary['max_impact']:.3f}")
+    
+    # Show recommendations
+    if results['simulation_analysis']['recommendations']:
+        print("\nRecommendations:")
+        for rec in results['simulation_analysis']['recommendations']:
+            print(f"  - {rec}")
+
+# Advanced usage with custom scenarios
+def custom_scenario_example():
+    """Example with custom stress test scenarios"""
+    
+    class CustomStressScenario(StressTestScenario):
+        def apply_stress(self, model, data: Dict[str, Any], intensity: float) -> Dict[str, Any]:
+            # Custom stress logic
+            stressed_data = data.copy()
+            # ... implementation
+            return stressed_data
+        
+        def measure_impact(self, original_results: Dict[str, Any], 
+                          stressed_results: Dict[str, Any]) -> Dict[str, Any]:
+            # Custom impact measurement
+            return {'custom_impact': 0.5}
+    
+    # Register custom scenario
+    simulation_engine = TrustSimulationEngine()
+    simulation_engine.register_scenario('custom_stress', CustomStressScenario(
+        'custom_stress', 'Custom stress test scenario'
+    ))
+
+  
+# Use in evaluation
+# ... implementation
diff --git a/high_performance_system/core/trust_timeline.py b/high_performance_system/core/trust_timeline.py
new file mode 100644
index 0000000..53814a3
--- /dev/null
+++ b/high_performance_system/core/trust_timeline.py
@@ -0,0 +1,165 @@
+# src/evolution/trust_timeline.py
+"""
+Trust Evolution System - Track how trust changes over time, contexts, and model updates
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional
+import plotly.graph_objects as go
+import plotly.express as px
+
+class TrustEvolutionTracker:
+    """Tracks trust evolution across multiple dimensions and time periods"""
+    
+    def __init__(self):
+        self.trust_history = pd.DataFrame()
+        self.evolution_patterns = {}
+        self.anomaly_detectors = {}
+    
+    def track_evaluation(self, model_id: str, evaluation_results: Dict[str, Any], 
+                        context: str = "general", timestamp: datetime = None):
+        """Track a trust evaluation in the timeline"""
+        if timestamp is None:
+            timestamp = datetime.now()
+        
+        # Extract trust metrics
+        record = {
+            'model_id': model_id,
+            'timestamp': timestamp,
+            'context': context,
+            'overall_trust': evaluation_results.get('overall_trust_score', 0.0),
+            'dimensions': evaluation_results.get('dimension_scores', {}),
+            'categories': evaluation_results.get('category_scores', {}),
+            'metadata': evaluation_results.get('metadata', {})
+        }
+        
+        # Add to history
+        self.trust_history = pd.concat([self.trust_history, pd.DataFrame([record])], 
+                                     ignore_index=True)
+        
+        # Update evolution patterns
+        self._update_evolution_patterns(model_id, record)
+    
+    def detect_trust_anomalies(self, model_id: str) -> List[Dict[str, Any]]:
+        """Detect anomalies in trust evolution"""
+        model_history = self.trust_history[self.trust_history['model_id'] == model_id]
+        
+        anomalies = []
+        for dimension in ['overall_trust'] + list(model_history['dimensions'].iloc[0].keys()):
+            scores = model_history[dimension].values if dimension == 'overall_trust' else \
+                    [d.get(dimension, 0) for d in model_history['dimensions']]
+            
+            # Statistical anomaly detection
+            mean_score = np.mean(scores)
+            std_score = np.std(scores)
+            
+            # Check for recent significant drops
+            if len(scores) > 3:
+                recent_drop = scores[-1] < (mean_score - 2 * std_score)
+                if recent_drop:
+                    anomalies.append({
+                        'type': 'significant_drop',
+                        'dimension': dimension,
+                        'current_score': scores[-1],
+                        'historical_mean': mean_score,
+                        'severity': 'high' if scores[-1] < (mean_score - 3 * std_score) else 'medium',
+                        'timestamp': model_history.iloc[-1]['timestamp']
+                    })
+        
+        return anomalies
+    
+    def predict_trust_trajectory(self, model_id: str, days_ahead: int = 30) -> Dict[str, Any]:
+        """Predict future trust trajectory using time series analysis"""
+        model_history = self.trust_history[self.trust_history['model_id'] == model_id]
+        
+        if len(model_history) < 5:
+            return {'status': 'insufficient_data', 'prediction': None}
+        
+        # Simple linear regression for prediction
+        timestamps = [(t - model_history.iloc[0]['timestamp']).days 
+                     for t in model_history['timestamp']]
+        overall_scores = model_history['overall_trust'].values
+        
+        # Linear regression
+        if len(set(timestamps)) > 1:  # Avoid division by zero
+            slope = np.polyfit(timestamps, overall_scores, 1)[0]
+            predicted_score = overall_scores[-1] + (slope * days_ahead)
+            
+            return {
+                'status': 'success',
+                'current_score': overall_scores[-1],
+                'predicted_score': max(0, min(1, predicted_score)),  # Clamp between 0-1
+                'trend': 'improving' if slope > 0 else 'declining' if slope < 0 else 'stable',
+                'confidence': min(1.0, len(timestamps) / 20.0)  # Confidence increases with more data
+            }
+        
+        return {'status': 'insufficient_variance', 'prediction': None}
+    
+    def generate_evolution_report(self, model_id: str) -> Dict[str, Any]:
+        """Generate comprehensive trust evolution report"""
+        model_history = self.trust_history[self.trust_history['model_id'] == model_id]
+        
+        if len(model_history) == 0:
+            return {'status': 'no_data', 'report': None}
+        
+        # Calculate evolution metrics
+        first_score = model_history.iloc[0]['overall_trust']
+        last_score = model_history.iloc[-1]['overall_trust']
+        score_change = last_score - first_score
+        
+        # Dimension evolution
+        dimension_evolution = {}
+        if len(model_history) > 1:
+            first_dims = model_history.iloc[0]['dimensions']
+            last_dims = model_history.iloc[-1]['dimensions']
+            
+            for dim in set(first_dims.keys()) | set(last_dims.keys()):
+                first_val = first_dims.get(dim, 0)
+                last_val = last_dims.get(dim, 0)
+                dimension_evolution[dim] = {
+                    'change': last_val - first_val,
+                    'percentage_change': ((last_val - first_val) / first_val * 100) if first_val > 0 else 0
+                }
+        
+        return {
+            'status': 'success',
+            'model_id': model_id,
+            'evaluation_count': len(model_history),
+            'time_span': (model_history.iloc[-1]['timestamp'] - model_history.iloc[0]['timestamp']).days,
+            'overall_evolution': {
+                'initial_score': first_score,
+                'current_score': last_score,
+                'absolute_change': score_change,
+                'percentage_change': (score_change / first_score * 100) if first_score > 0 else 0
+            },
+            'dimension_evolution': dimension_evolution,
+            'anomalies_detected': self.detect_trust_anomalies(model_id),
+            'trajectory_prediction': self.predict_trust_trajectory(model_id)
+        }
+
+# Integration with main evaluator
+class EvolutionAwareEvaluator:
+    """Evaluator that tracks trust evolution"""
+    
+    def __init__(self):
+        self.trust_tracker = TrustEvolutionTracker()
+        # ... other initialization
+    
+    def evaluate_with_evolution_tracking(self, model, data, model_id: str, 
+                                       context: str = "general") -> Dict[str, Any]:
+        """Execute evaluation with evolution tracking"""
+        results = self.evaluate_comprehensive_trust(model, data)
+        
+        # Track in evolution system
+        self.trust_tracker.track_evaluation(model_id, results, context)
+        
+        # Add evolution insights
+        results['evolution_insights'] = {
+            'anomalies': self.trust_tracker.detect_trust_anomalies(model_id),
+            'trajectory': self.trust_tracker.predict_trust_trajectory(model_id),
+            'report': self.trust_tracker.generate_evolution_report(model_id)
+        }
+        
+        return results
diff --git a/high_performance_system/core/unified_plugin_manager.py b/high_performance_system/core/unified_plugin_manager.py
new file mode 100644
index 0000000..ee49156
--- /dev/null
+++ b/high_performance_system/core/unified_plugin_manager.py
@@ -0,0 +1,188 @@
+"""Unified plugin system for LLM trust evaluation"""
+
+import sys
+import os
+from typing import Dict, Any, List, Optional, Callable
+from abc import ABC, abstractmethod
+import logging
+
+logger = logging.getLogger(__name__)
+
+class UnifiedTrustPlugin(ABC):
+    """Base plugin interface for unified trust evaluation"""
+    
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Plugin name"""
+        pass
+    
+    @property
+    @abstractmethod
+    def category(self) -> str:
+        """Plugin category (reliability, safety, fairness, etc.)"""
+        pass
+    
+    @property
+    @abstractmethod
+    def model_types(self) -> List[str]:
+        """Supported model types"""
+        pass
+    
+    @abstractmethod
+    def evaluate(self, model, data: Dict[str, Any], **kwargs) -> Dict[str, Any]:
+        """Execute evaluation"""
+        pass
+    
+    @abstractmethod
+    def is_available(self) -> bool:
+        """Check if plugin dependencies are available"""
+        pass
+
+class UnifiedPluginManager:
+    """Manages plugins across OpenTrustEval and TrustLLM"""
+    
+    def __init__(self):
+        self.plugins: Dict[str, UnifiedTrustPlugin] = {}
+        self.adapters: Dict[str, Any] = {}
+        self._initialize_adapters()
+    
+    def _initialize_adapters(self):
+        """Initialize external tool adapters"""
+        # TrustLLM adapter
+        try:
+            from src.integration.trustllm_adapter import TrustLLMAdapter
+            self.adapters['trustllm'] = TrustLLMAdapter()
+            logger.info("✓ TrustLLM adapter initialized")
+        except Exception as e:
+            logger.warning(f"⚠ TrustLLM adapter not available: {e}")
+        
+        # CleanLab adapter
+        try:
+            from src.integration.cleanlab_adapter import CleanLabAdapter
+            self.adapters['cleanlab'] = CleanLabAdapter()
+            logger.info("✓ CleanLab adapter initialized")
+        except Exception as e:
+            logger.warning(f"⚠ CleanLab adapter not available: {e}")
+        
+        # DeepChecks adapter
+        try:
+            from src.integration.deepchecks_adapter import DeepChecksAdapter
+            self.adapters['deepchecks'] = DeepChecksAdapter()
+            logger.info("✓ DeepChecks adapter initialized")
+        except Exception as e:
+            logger.warning(f"⚠ DeepChecks adapter not available: {e}")
+    
+    def register_plugin(self, plugin: UnifiedTrustPlugin):
+        """Register a trust evaluation plugin"""
+        if plugin.is_available():
+            self.plugins[plugin.name] = plugin
+            logger.info(f"✓ Registered plugin: {plugin.name}")
+        else:
+            logger.warning(f"⚠ Plugin not available: {plugin.name}")
+    
+    def get_compatible_plugins(self, model_type: str) -> List[UnifiedTrustPlugin]:
+        """Get plugins compatible with specific model type"""
+        compatible = []
+        for plugin in self.plugins.values():
+            if model_type in plugin.model_types or 'all' in plugin.model_types:
+                compatible.append(plugin)
+        return compatible
+    
+    def execute_evaluation(self, model, data: Dict[str, Any], 
+                          model_type: str = 'llm', 
+                          categories: List[str] = None) -> Dict[str, Any]:
+        """Execute comprehensive evaluation using compatible plugins"""
+        compatible_plugins = self.get_compatible_plugins(model_type)
+        
+        if categories:
+            compatible_plugins = [p for p in compatible_plugins 
+                                if p.category in categories]
+        
+        results = {}
+        for plugin in compatible_plugins:
+            try:
+                plugin_result = plugin.evaluate(model, data)
+                results[plugin.name] = {
+                    'success': True,
+                    'result': plugin_result,
+                    'plugin_info': {
+                        'name': plugin.name,
+                        'category': plugin.category,
+                        'model_types': plugin.model_types
+                    }
+                }
+            except Exception as e:
+                results[plugin.name] = {
+                    'success': False,
+                    'error': str(e),
+                    'plugin_info': {
+                        'name': plugin.name,
+                        'category': plugin.category
+                    }
+                }
+        
+        return self._aggregate_results(results)
+    
+    def _aggregate_results(self, plugin_results: Dict[str, Any]) -> Dict[str, Any]:
+        """Aggregate results from multiple plugins"""
+        aggregated = {
+            'dimension_scores': {},
+            'category_scores': {},
+            'plugin_performance': {},
+            'conflicts': [],
+            'overall_score': 0.0
+        }
+        
+        # Collect dimension scores
+        for plugin_name, result in plugin_results.items():
+            if result.get('success', False):
+                plugin_data = result.get('result', {})
+                if 'score' in plugin_data:
+                    # Single score plugin
+                    aggregated['plugin_performance'][plugin_name] = plugin_data['score']
+                elif 'dimension_scores' in plugin_data:
+                    # Multi-dimension plugin
+                    for dim_name, dim_score in plugin_data['dimension_scores'].items():
+                        if isinstance(dim_score, dict) and 'score' in dim_score:
+                            aggregated['dimension_scores'][dim_name] = dim_score['score']
+                        else:
+                            aggregated['dimension_scores'][dim_name] = dim_score
+        
+        # Calculate category scores
+        category_scores = {}
+        for dim_name, score in aggregated['dimension_scores'].items():
+            category = self._infer_category(dim_name)
+            if category not in category_scores:
+                category_scores[category] = []
+            category_scores[category].append(score)
+        
+        for category, scores in category_scores.items():
+            aggregated['category_scores'][category] = sum(scores) / len(scores)
+        
+        # Calculate overall score
+        if aggregated['dimension_scores']:
+            aggregated['overall_score'] = sum(aggregated['dimension_scores'].values()) / len(aggregated['dimension_scores'])
+        elif aggregated['plugin_performance']:
+            aggregated['overall_score'] = sum(aggregated['plugin_performance'].values()) / len(aggregated['plugin_performance'])
+        
+        return aggregated
+    
+    def _infer_category(self, dimension_name: str) -> str:
+        """Infer category from dimension name"""
+        dimension_name = dimension_name.lower()
+        if any(word in dimension_name for word in ['truth', 'fact', 'accur']):
+            return 'reliability'
+        elif any(word in dimension_name for word in ['safe', 'harm', 'toxic']):
+            return 'safety'
+        elif any(word in dimension_name for word in ['fair', 'bias', 'discrim']):
+            return 'fairness'
+        elif any(word in dimension_name for word in ['consist', 'stable']):
+            return 'consistency'
+        elif any(word in dimension_name for word in ['explain', 'interpret']):
+            return 'explainability'
+        else:
+            return 'general'
+
+# Global plugin manager instance
+plugin_manager = UnifiedPluginManager()
diff --git a/launch_workflow_webui.py b/launch_workflow_webui.py
new file mode 100644
index 0000000..03efd58
--- /dev/null
+++ b/launch_workflow_webui.py
@@ -0,0 +1,16 @@
+import subprocess
+import sys
+
+def main():
+    """
+    Launches the Unified Workflow Web UI.
+    """
+    try:
+        subprocess.run([sys.executable, "-m", "streamlit", "run", "workflow_webui.py"])
+    except FileNotFoundError:
+        print("Error: 'streamlit' command not found. Please make sure Streamlit is installed.")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/llm_reliability_plugin.py b/plugins/llm_reliability_plugin.py
new file mode 100644
index 0000000..33ffa58
--- /dev/null
+++ b/plugins/llm_reliability_plugin.py
@@ -0,0 +1,86 @@
+"""LLM-specific reliability evaluation plugin"""
+
+from src.core.unified_plugin_manager import UnifiedTrustPlugin
+from typing import Dict, Any, List
+import numpy as np
+import logging
+
+logger = logging.getLogger(__name__)
+
+class LLMReliabilityPlugin(UnifiedTrustPlugin):
+    """LLM-specific reliability evaluation plugin"""
+    
+    def __init__(self):
+        self.name = "llm_reliability"
+        self.category = "reliability"
+        self.model_types = ["llm", "language_model", "transformer", "all"]
+        self.adapter = None
+        self._initialize_adapter()
+    
+    def _initialize_adapter(self):
+        """Initialize adapter for external tools"""
+        try:
+            from src.integration.cleanlab_adapter import CleanLabAdapter
+            self.adapter = CleanLabAdapter()
+        except Exception as e:
+            logger.debug(f"CleanLab adapter not available: {e}")
+            self.adapter = None
+    
+    def is_available(self) -> bool:
+        """Plugin is always available (fallback methods)"""
+        return True
+    
+    def evaluate(self, model, data: Dict[str, Any], **kwargs) -> Dict[str, Any]:
+        """Execute LLM reliability evaluation"""
+        try:
+            # Extract prompts and generate responses
+            prompts = data.get('prompts', [])
+            if not prompts:
+                return self._basic_reliability_evaluation()
+            
+            # Generate responses
+            responses = []
+            for prompt in prompts:
+                response = self._generate_response(model, prompt)
+                responses.append(response)
+            
+            # Evaluate different aspects of reliability
+            factual_accuracy = self._evaluate_factual_accuracy(data, responses)
+            consistency_score = self._evaluate_consistency(model, prompts, responses)
+            confidence_calibration = self._evaluate_confidence_calibration(model, prompts, responses)
+            
+            # Calculate overall reliability score
+            reliability_components = [
+                factual_accuracy,
+                consistency_score,
+                confidence_calibration
+            ]
+            overall_reliability = sum(reliability_components) / len(reliability_components)
+            
+            return {
+                'score': float(overall_reliability),
+                'dimension_scores': {
+                    'factual_accuracy': factual_accuracy,
+                    'consistency': consistency_score,
+                    'confidence_calibration': confidence_calibration
+                },
+                'details': {
+                    'evaluated_prompts': len(prompts),
+                    'response_consistency': self._analyze_response_patterns(responses)
+                },
+                'metadata': {
+                    'evaluator': 'llm_reliability_plugin',
+                    'timestamp': self._get_timestamp()
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"LLM reliability evaluation failed: {e}")
+            return self._basic_reliability_evaluation()
+    
+    def _generate_response(self, model, prompt: str) -> str:
+        """Generate response from model"""
+        try:
+            if hasattr(model, 'generate'):
+                return model.generate(prompt)
+            elif
diff --git a/plugins/trustllm_plugin.py b/plugins/trustllm_plugin.py
new file mode 100644
index 0000000..3b82eb6
--- /dev/null
+++ b/plugins/trustllm_plugin.py
@@ -0,0 +1,141 @@
+"""TrustLLM comprehensive trust evaluation plugin"""
+
+from src.core.unified_plugin_manager import UnifiedTrustPlugin
+from typing import Dict, Any, List
+import logging
+
+logger = logging.getLogger(__name__)
+
+class TrustLLMComprehensivePlugin(UnifiedTrustPlugin):
+    """Comprehensive TrustLLM evaluation plugin"""
+    
+    def __init__(self):
+        self.name = "trustllm_comprehensive"
+        self.category = "overall"
+        self.model_types = ["llm", "language_model", "transformer"]
+        self.adapter = None
+        self._initialize_adapter()
+    
+    def _initialize_adapter(self):
+        """Initialize TrustLLM adapter"""
+        try:
+            from src.integration.trustllm_adapter import TrustLLMAdapter
+            self.adapter = TrustLLMAdapter()
+        except Exception as e:
+            logger.error(f"Failed to initialize TrustLLM adapter: {e}")
+            self.adapter = None
+    
+    def is_available(self) -> bool:
+        """Check if plugin is available"""
+        return self.adapter is not None and self.adapter.is_available()
+    
+    def evaluate(self, model, data: Dict[str, Any], **kwargs) -> Dict[str, Any]:
+        """Execute comprehensive TrustLLM evaluation"""
+        if not self.is_available():
+            return self._fallback_evaluation()
+        
+        try:
+            # Prepare data for TrustLLM
+            trustllm_data = self._prepare_trustllm_data(data)
+            
+            # Execute TrustLLM evaluations
+            results = {}
+            
+            # Truthfulness evaluation
+            truthfulness_result = self.adapter.evaluate_truthfulness(model, trustllm_data)
+            results['truthfulness'] = truthfulness_result
+            
+            # Safety evaluation
+            safety_result = self.adapter.evaluate_safety(model, trustllm_data)
+            results['safety'] = safety_result
+            
+            # Hallucination evaluation
+            hallucination_result = self.adapter.evaluate_hallucination(model, trustllm_data)
+            results['hallucination'] = hallucination_result
+            
+            # Privacy evaluation
+            privacy_result = self.adapter.evaluate_privacy(model, trustllm_data)
+            results['privacy'] = privacy_result
+            
+            # Toxicity evaluation
+            toxicity_result = self.adapter.evaluate_toxicity(model, trustllm_data)
+            results['toxicity'] = toxicity_result
+            
+            # Calculate comprehensive score
+            dimension_scores = {}
+            for dim_name, dim_result in results.items():
+                if isinstance(dim_result, dict) and 'score' in dim_result:
+                    dimension_scores[dim_name] = dim_result['score']
+            
+            comprehensive_score = sum(dimension_scores.values()) / len(dimension_scores) if dimension_scores else 0.5
+            
+            return {
+                'score': float(comprehensive_score),
+                'dimension_scores': dimension_scores,
+                'detailed_results': results,
+                'metadata': {
+                    'evaluator': 'trustllm',
+                    'timestamp': self._get_timestamp(),
+                    'dimensions_evaluated': list(dimension_scores.keys())
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"TrustLLM evaluation failed: {e}")
+            return self._fallback_evaluation()
+    
+    def _prepare_trustllm_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Prepare data in TrustLLM format"""
+        # Convert OpenTrustEval data format to TrustLLM format
+        trustllm_data = {}
+        
+        # Map common fields
+        if 'prompts' in data:
+            trustllm_data['prompts'] = data['prompts']
+        if 'responses' in data:
+            trustllm_data['responses'] = data['responses']
+        if 'ground_truth' in data:
+            trustllm_data['ground_truth'] = data['ground_truth']
+        if 'contexts' in data:
+            trustllm_data['contexts'] = data['contexts']
+        
+        return trustllm_data
+    
+    def _fallback_evaluation(self) -> Dict[str, Any]:
+        """Fallback evaluation when TrustLLM unavailable"""
+        return {
+            'score': 0.5,
+            'dimension_scores': {
+                'truthfulness': 0.5,
+                'safety': 0.7,  # Conservative safety default
+                'hallucination': 0.5,
+                'privacy': 0.6,
+                'toxicity': 0.7
+            },
+            'detailed_results': {},
+            'metadata': {
+                'evaluator': 'fallback',
+                'warning': 'TrustLLM not available, using default scores'
+            }
+        }
+    
+    def _get_timestamp(self) -> str:
+        """Get current timestamp"""
+        from datetime import datetime
+        return datetime.now().isoformat()
+
+# Register the plugin
+def register_trustllm_plugin(plugin_manager):
+    """Register TrustLLM plugin with plugin manager"""
+    plugin = TrustLLMComprehensivePlugin()
+    if plugin.is_available():
+        plugin_manager.register_plugin(plugin)
+        logger.info("✓ TrustLLM comprehensive plugin registered")
+    else:
+        logger.warning("⚠ TrustLLM comprehensive plugin not available")
+
+# Auto-registration
+try:
+    register_trustllm_plugin(plugin_manager)
+except Exception as e:
+    logger.error(f"Failed to register TrustLLM plugin: {e}")
diff --git a/tests/=0.3.0 b/tests/=0.3.0
new file mode 100644
index 0000000..9aedf66
--- /dev/null
+++ b/tests/=0.3.0
@@ -0,0 +1,43 @@
+Collecting streamlit-option-menu
+  Downloading streamlit_option_menu-0.4.0-py3-none-any.whl.metadata (2.5 kB)
+Requirement already satisfied: streamlit>=1.36 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit-option-menu) (1.47.1)
+Requirement already satisfied: altair<6,>=4.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (5.5.0)
+Requirement already satisfied: blinker<2,>=1.5.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (1.9.0)
+Requirement already satisfied: cachetools<7,>=4.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (5.5.2)
+Requirement already satisfied: click<9,>=7.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (8.2.1)
+Requirement already satisfied: numpy<3,>=1.23 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (1.26.4)
+Requirement already satisfied: packaging<26,>=20 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (25.0)
+Requirement already satisfied: pandas<3,>=1.4.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (2.2.2)
+Requirement already satisfied: pillow<12,>=7.1.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (11.3.0)
+Requirement already satisfied: protobuf<7,>=3.20 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (4.25.8)
+Requirement already satisfied: pyarrow>=7.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (21.0.0)
+Requirement already satisfied: requests<3,>=2.27 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (2.31.0)
+Requirement already satisfied: tenacity<10,>=8.1.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (9.1.2)
+Requirement already satisfied: toml<2,>=0.10.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (0.10.2)
+Requirement already satisfied: typing-extensions<5,>=4.4.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (4.14.1)
+Requirement already satisfied: watchdog<7,>=2.1.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (6.0.0)
+Requirement already satisfied: gitpython!=3.1.19,<4,>=3.0.7 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (3.1.45)
+Requirement already satisfied: pydeck<1,>=0.8.0b4 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (0.9.1)
+Requirement already satisfied: tornado!=6.5.0,<7,>=6.0.3 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from streamlit>=1.36->streamlit-option-menu) (6.5.1)
+Requirement already satisfied: jinja2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (3.1.6)
+Requirement already satisfied: jsonschema>=3.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (4.25.0)
+Requirement already satisfied: narwhals>=1.14.2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (1.48.1)
+Requirement already satisfied: gitdb<5,>=4.0.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from gitpython!=3.1.19,<4,>=3.0.7->streamlit>=1.36->streamlit-option-menu) (4.0.12)
+Requirement already satisfied: smmap<6,>=3.0.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit>=1.36->streamlit-option-menu) (5.0.2)
+Requirement already satisfied: python-dateutil>=2.8.2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas<3,>=1.4.0->streamlit>=1.36->streamlit-option-menu) (2.9.0.post0)
+Requirement already satisfied: pytz>=2020.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas<3,>=1.4.0->streamlit>=1.36->streamlit-option-menu) (2025.2)
+Requirement already satisfied: tzdata>=2022.7 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas<3,>=1.4.0->streamlit>=1.36->streamlit-option-menu) (2025.2)
+Requirement already satisfied: charset-normalizer<4,>=2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (3.4.2)
+Requirement already satisfied: idna<4,>=2.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (3.10)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (2.5.0)
+Requirement already satisfied: certifi>=2017.4.17 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (2025.7.14)
+Requirement already satisfied: MarkupSafe>=2.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from jinja2->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (3.0.2)
+Requirement already satisfied: attrs>=22.2.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (25.3.0)
+Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (2025.4.1)
+Requirement already satisfied: referencing>=0.28.4 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (0.36.2)
+Requirement already satisfied: rpds-py>=0.7.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (0.26.0)
+Requirement already satisfied: six>=1.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas<3,>=1.4.0->streamlit>=1.36->streamlit-option-menu) (1.17.0)
+Downloading streamlit_option_menu-0.4.0-py3-none-any.whl (829 kB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 829.3/829.3 kB 22.1 MB/s eta 0:00:00
+Installing collected packages: streamlit-option-menu
+Successfully installed streamlit-option-menu-0.4.0
diff --git a/tests/=0.4.27 b/tests/=0.4.27
new file mode 100644
index 0000000..1102861
--- /dev/null
+++ b/tests/=0.4.27
@@ -0,0 +1 @@
+Requirement already satisfied: python-magic in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (0.4.27)
diff --git a/tests/=1.1.0 b/tests/=1.1.0
new file mode 100644
index 0000000..55de4d4
--- /dev/null
+++ b/tests/=1.1.0
@@ -0,0 +1,12 @@
+Collecting pyreadstat
+  Downloading pyreadstat-1.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)
+Requirement already satisfied: pandas>=1.2.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pyreadstat) (2.2.2)
+Requirement already satisfied: numpy>=1.26.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas>=1.2.0->pyreadstat) (1.26.4)
+Requirement already satisfied: python-dateutil>=2.8.2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas>=1.2.0->pyreadstat) (2.9.0.post0)
+Requirement already satisfied: pytz>=2020.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas>=1.2.0->pyreadstat) (2025.2)
+Requirement already satisfied: tzdata>=2022.7 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pandas>=1.2.0->pyreadstat) (2025.2)
+Requirement already satisfied: six>=1.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.2.0->pyreadstat) (1.17.0)
+Downloading pyreadstat-1.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (661 kB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 661.2/661.2 kB 18.6 MB/s eta 0:00:00
+Installing collected packages: pyreadstat
+Successfully installed pyreadstat-1.3.0
diff --git a/tests/=1.26.0 b/tests/=1.26.0
new file mode 100644
index 0000000..0670781
--- /dev/null
+++ b/tests/=1.26.0
@@ -0,0 +1,19 @@
+Collecting boto3
+  Downloading boto3-1.39.13-py3-none-any.whl.metadata (6.7 kB)
+Collecting botocore<1.40.0,>=1.39.13 (from boto3)
+  Downloading botocore-1.39.13-py3-none-any.whl.metadata (5.7 kB)
+Collecting jmespath<2.0.0,>=0.7.1 (from boto3)
+  Downloading jmespath-1.0.1-py3-none-any.whl.metadata (7.6 kB)
+Collecting s3transfer<0.14.0,>=0.13.0 (from boto3)
+  Downloading s3transfer-0.13.1-py3-none-any.whl.metadata (1.7 kB)
+Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from botocore<1.40.0,>=1.39.13->boto3) (2.9.0.post0)
+Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from botocore<1.40.0,>=1.39.13->boto3) (2.5.0)
+Requirement already satisfied: six>=1.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.40.0,>=1.39.13->boto3) (1.17.0)
+Downloading boto3-1.39.13-py3-none-any.whl (139 kB)
+Downloading botocore-1.39.13-py3-none-any.whl (13.9 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.9/13.9 MB 43.6 MB/s eta 0:00:00
+Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)
+Downloading s3transfer-0.13.1-py3-none-any.whl (85 kB)
+Installing collected packages: jmespath, botocore, s3transfer, boto3
+
+Successfully installed boto3-1.39.13 botocore-1.39.13 jmespath-1.0.1 s3transfer-0.13.1
diff --git a/tests/=1.3.1 b/tests/=1.3.1
new file mode 100644
index 0000000..47f8c67
--- /dev/null
+++ b/tests/=1.3.1
@@ -0,0 +1,45 @@
+Collecting pydrive
+  Downloading PyDrive-1.3.1.tar.gz (987 kB)
+     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 987.4/987.4 kB 26.0 MB/s eta 0:00:00
+  Preparing metadata (setup.py): started
+  Preparing metadata (setup.py): finished with status 'done'
+Collecting google-api-python-client>=1.2 (from pydrive)
+  Downloading google_api_python_client-2.177.0-py3-none-any.whl.metadata (7.0 kB)
+Collecting oauth2client>=4.0.0 (from pydrive)
+  Downloading oauth2client-4.1.3-py2.py3-none-any.whl.metadata (1.2 kB)
+Requirement already satisfied: PyYAML>=3.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from pydrive) (6.0.1)
+Collecting httplib2<1.0.0,>=0.19.0 (from google-api-python-client>=1.2->pydrive)
+  Downloading httplib2-0.22.0-py3-none-any.whl.metadata (2.6 kB)
+Requirement already satisfied: google-auth!=2.24.0,!=2.25.0,<3.0.0,>=1.32.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-python-client>=1.2->pydrive) (2.40.3)
+Collecting google-auth-httplib2<1.0.0,>=0.2.0 (from google-api-python-client>=1.2->pydrive)
+  Downloading google_auth_httplib2-0.2.0-py2.py3-none-any.whl.metadata (2.2 kB)
+Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-python-client>=1.2->pydrive) (2.25.1)
+Requirement already satisfied: uritemplate<5,>=3.0.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-python-client>=1.2->pydrive) (4.2.0)
+Requirement already satisfied: googleapis-common-protos<2.0.0,>=1.56.2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (1.70.0)
+Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<7.0.0,>=3.19.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (4.25.8)
+Requirement already satisfied: proto-plus<2.0.0,>=1.22.3 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (1.26.1)
+Requirement already satisfied: requests<3.0.0,>=2.18.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (2.31.0)
+Requirement already satisfied: cachetools<6.0,>=2.0.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0,>=1.32.0->google-api-python-client>=1.2->pydrive) (5.5.2)
+Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0,>=1.32.0->google-api-python-client>=1.2->pydrive) (0.4.2)
+Requirement already satisfied: rsa<5,>=3.1.4 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0,>=1.32.0->google-api-python-client>=1.2->pydrive) (4.9.1)
+Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from httplib2<1.0.0,>=0.19.0->google-api-python-client>=1.2->pydrive) (3.2.3)
+Requirement already satisfied: charset-normalizer<4,>=2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (3.4.2)
+Requirement already satisfied: idna<4,>=2.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (3.10)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (2.5.0)
+Requirement already satisfied: certifi>=2017.4.17 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0,>=1.31.5->google-api-python-client>=1.2->pydrive) (2025.7.14)
+Requirement already satisfied: pyasn1>=0.1.3 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from rsa<5,>=3.1.4->google-auth!=2.24.0,!=2.25.0,<3.0.0,>=1.32.0->google-api-python-client>=1.2->pydrive) (0.6.1)
+Requirement already satisfied: six>=1.6.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from oauth2client>=4.0.0->pydrive) (1.17.0)
+Downloading google_api_python_client-2.177.0-py3-none-any.whl (13.7 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.7/13.7 MB 92.1 MB/s eta 0:00:00
+Downloading google_auth_httplib2-0.2.0-py2.py3-none-any.whl (9.3 kB)
+Downloading httplib2-0.22.0-py3-none-any.whl (96 kB)
+Downloading oauth2client-4.1.3-py2.py3-none-any.whl (98 kB)
+Building wheels for collected packages: pydrive
+  Building wheel for pydrive (setup.py): started
+  Building wheel for pydrive (setup.py): finished with status 'done'
+  Created wheel for pydrive: filename=pydrive-1.3.1-py3-none-any.whl size=27539 sha256=0837f0594e858cf75dd820443de7c32c35f56eb6d5136425711b8aed656701ea
+  Stored in directory: /home/jules/.cache/pip/wheels/6c/10/da/a5b513f5b3916fc391c20ee7b4633e5cf3396d570cdd74970f
+Successfully built pydrive
+Installing collected packages: httplib2, oauth2client, google-auth-httplib2, google-api-python-client, pydrive
+
+Successfully installed google-api-python-client-2.177.0 google-auth-httplib2-0.2.0 httplib2-0.22.0 oauth2client-4.1.3 pydrive-1.3.1
diff --git a/tests/=2.0.1 b/tests/=2.0.1
new file mode 100644
index 0000000..478e234
--- /dev/null
+++ b/tests/=2.0.1
@@ -0,0 +1 @@
+Requirement already satisfied: xlrd in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (2.0.2)
diff --git a/tests/=2.8.0 b/tests/=2.8.0
new file mode 100644
index 0000000..23c089b
--- /dev/null
+++ b/tests/=2.8.0
@@ -0,0 +1,45 @@
+Collecting google-cloud-storage
+  Downloading google_cloud_storage-3.2.0-py3-none-any.whl.metadata (13 kB)
+Collecting google-auth<3.0.0,>=2.26.1 (from google-cloud-storage)
+  Downloading google_auth-2.40.3-py2.py3-none-any.whl.metadata (6.2 kB)
+Collecting google-api-core<3.0.0,>=2.15.0 (from google-cloud-storage)
+  Downloading google_api_core-2.25.1-py3-none-any.whl.metadata (3.0 kB)
+Collecting google-cloud-core<3.0.0,>=2.4.2 (from google-cloud-storage)
+  Downloading google_cloud_core-2.4.3-py2.py3-none-any.whl.metadata (2.7 kB)
+Collecting google-resumable-media<3.0.0,>=2.7.2 (from google-cloud-storage)
+  Downloading google_resumable_media-2.7.2-py2.py3-none-any.whl.metadata (2.2 kB)
+Requirement already satisfied: requests<3.0.0,>=2.22.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-cloud-storage) (2.31.0)
+Collecting google-crc32c<2.0.0,>=1.1.3 (from google-cloud-storage)
+  Downloading google_crc32c-1.7.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.3 kB)
+Collecting googleapis-common-protos<2.0.0,>=1.56.2 (from google-api-core<3.0.0,>=2.15.0->google-cloud-storage)
+  Downloading googleapis_common_protos-1.70.0-py3-none-any.whl.metadata (9.3 kB)
+Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<7.0.0,>=3.19.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-core<3.0.0,>=2.15.0->google-cloud-storage) (4.25.8)
+Requirement already satisfied: proto-plus<2.0.0,>=1.22.3 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from google-api-core<3.0.0,>=2.15.0->google-cloud-storage) (1.26.1)
+Collecting cachetools<6.0,>=2.0.0 (from google-auth<3.0.0,>=2.26.1->google-cloud-storage)
+  Downloading cachetools-5.5.2-py3-none-any.whl.metadata (5.4 kB)
+Collecting pyasn1-modules>=0.2.1 (from google-auth<3.0.0,>=2.26.1->google-cloud-storage)
+  Downloading pyasn1_modules-0.4.2-py3-none-any.whl.metadata (3.5 kB)
+Collecting rsa<5,>=3.1.4 (from google-auth<3.0.0,>=2.26.1->google-cloud-storage)
+  Downloading rsa-4.9.1-py3-none-any.whl.metadata (5.6 kB)
+Requirement already satisfied: charset-normalizer<4,>=2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.22.0->google-cloud-storage) (3.4.2)
+Requirement already satisfied: idna<4,>=2.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.22.0->google-cloud-storage) (3.10)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.22.0->google-cloud-storage) (2.5.0)
+Requirement already satisfied: certifi>=2017.4.17 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests<3.0.0,>=2.22.0->google-cloud-storage) (2025.7.14)
+Requirement already satisfied: pyasn1>=0.1.3 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from rsa<5,>=3.1.4->google-auth<3.0.0,>=2.26.1->google-cloud-storage) (0.6.1)
+Downloading google_cloud_storage-3.2.0-py3-none-any.whl (176 kB)
+Downloading google_api_core-2.25.1-py3-none-any.whl (160 kB)
+Downloading google_auth-2.40.3-py2.py3-none-any.whl (216 kB)
+Downloading cachetools-5.5.2-py3-none-any.whl (10 kB)
+Downloading google_cloud_core-2.4.3-py2.py3-none-any.whl (29 kB)
+Downloading google_crc32c-1.7.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (32 kB)
+Downloading google_resumable_media-2.7.2-py2.py3-none-any.whl (81 kB)
+Downloading googleapis_common_protos-1.70.0-py3-none-any.whl (294 kB)
+Downloading rsa-4.9.1-py3-none-any.whl (34 kB)
+Downloading pyasn1_modules-0.4.2-py3-none-any.whl (181 kB)
+Installing collected packages: rsa, pyasn1-modules, googleapis-common-protos, google-crc32c, cachetools, google-resumable-media, google-auth, google-api-core, google-cloud-core, google-cloud-storage
+  Attempting uninstall: cachetools
+    Found existing installation: cachetools 6.1.0
+    Uninstalling cachetools-6.1.0:
+      Successfully uninstalled cachetools-6.1.0
+
+Successfully installed cachetools-5.5.2 google-api-core-2.25.1 google-auth-2.40.3 google-cloud-core-2.4.3 google-cloud-storage-3.2.0 google-crc32c-1.7.1 google-resumable-media-2.7.2 googleapis-common-protos-1.70.0 pyasn1-modules-0.4.2 rsa-4.9.1
diff --git a/tests/=3.0.10 b/tests/=3.0.10
new file mode 100644
index 0000000..1b5b713
--- /dev/null
+++ b/tests/=3.0.10
@@ -0,0 +1,9 @@
+Collecting openpyxl
+  Downloading openpyxl-3.1.5-py2.py3-none-any.whl.metadata (2.5 kB)
+Collecting et-xmlfile (from openpyxl)
+  Downloading et_xmlfile-2.0.0-py3-none-any.whl.metadata (2.7 kB)
+Downloading openpyxl-3.1.5-py2.py3-none-any.whl (250 kB)
+Downloading et_xmlfile-2.0.0-py3-none-any.whl (18 kB)
+Installing collected packages: et-xmlfile, openpyxl
+
+Successfully installed et-xmlfile-2.0.0 openpyxl-3.1.5
diff --git a/tests/=3.7.0 b/tests/=3.7.0
new file mode 100644
index 0000000..96f4768
--- /dev/null
+++ b/tests/=3.7.0
@@ -0,0 +1,24 @@
+Collecting tables
+  Downloading tables-3.10.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.0 kB)
+Requirement already satisfied: numpy>=1.20.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from tables) (1.26.4)
+Requirement already satisfied: numexpr>=2.6.2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from tables) (2.11.0)
+Requirement already satisfied: packaging in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from tables) (25.0)
+Requirement already satisfied: py-cpuinfo in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from tables) (9.0.0)
+Collecting blosc2>=2.3.0 (from tables)
+  Downloading blosc2-3.6.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (7.0 kB)
+Requirement already satisfied: typing-extensions>=4.4.0 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from tables) (4.14.1)
+Requirement already satisfied: ndindex in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from blosc2>=2.3.0->tables) (1.10.0)
+Requirement already satisfied: msgpack in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from blosc2>=2.3.0->tables) (1.1.1)
+Requirement already satisfied: platformdirs in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from blosc2>=2.3.0->tables) (4.3.8)
+Requirement already satisfied: requests in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from blosc2>=2.3.0->tables) (2.31.0)
+Requirement already satisfied: charset-normalizer<4,>=2 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests->blosc2>=2.3.0->tables) (3.4.2)
+Requirement already satisfied: idna<4,>=2.5 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests->blosc2>=2.3.0->tables) (3.10)
+Requirement already satisfied: urllib3<3,>=1.21.1 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests->blosc2>=2.3.0->tables) (2.5.0)
+Requirement already satisfied: certifi>=2017.4.17 in /home/jules/.pyenv/versions/3.12.11/lib/python3.12/site-packages (from requests->blosc2>=2.3.0->tables) (2025.7.14)
+Downloading tables-3.10.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.5 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.5/7.5 MB 88.3 MB/s eta 0:00:00
+Downloading blosc2-3.6.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.4/4.4 MB 95.1 MB/s eta 0:00:00
+Installing collected packages: blosc2, tables
+
+Successfully installed blosc2-3.6.1 tables-3.10.2
diff --git a/tests/=4.9.0 b/tests/=4.9.0
new file mode 100644
index 0000000..6e0c295
--- /dev/null
+++ b/tests/=4.9.0
@@ -0,0 +1,6 @@
+Collecting lxml
+  Downloading lxml-6.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.6 kB)
+Downloading lxml-6.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (5.3 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.3/5.3 MB 29.9 MB/s eta 0:00:00
+Installing collected packages: lxml
+Successfully installed lxml-6.0.0
diff --git a/Custom_Cleanlab_Pipeline_Demo.ipynb b/tests/Custom_Cleanlab_Pipeline_Demo.ipynb
similarity index 100%
rename from Custom_Cleanlab_Pipeline_Demo.ipynb
rename to tests/Custom_Cleanlab_Pipeline_Demo.ipynb
diff --git a/cleanlab_plugin_test_output.txt b/tests/cleanlab_plugin_test_output.txt
similarity index 100%
rename from cleanlab_plugin_test_output.txt
rename to tests/cleanlab_plugin_test_output.txt
diff --git a/custom_cleanlab_pipeline.py b/tests/custom_cleanlab_pipeline.py
similarity index 100%
rename from custom_cleanlab_pipeline.py
rename to tests/custom_cleanlab_pipeline.py
diff --git a/custom_cleanlab_webui.py b/tests/custom_cleanlab_webui.py
similarity index 100%
rename from custom_cleanlab_webui.py
rename to tests/custom_cleanlab_webui.py
diff --git a/real_world_trust_scoring_report_20250723_224607.json b/tests/real_world_trust_scoring_report_20250723_224607.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_224607.json
rename to tests/real_world_trust_scoring_report_20250723_224607.json
diff --git a/real_world_trust_scoring_report_20250723_225126.json b/tests/real_world_trust_scoring_report_20250723_225126.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225126.json
rename to tests/real_world_trust_scoring_report_20250723_225126.json
diff --git a/real_world_trust_scoring_report_20250723_225309.json b/tests/real_world_trust_scoring_report_20250723_225309.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225309.json
rename to tests/real_world_trust_scoring_report_20250723_225309.json
diff --git a/real_world_trust_scoring_report_20250723_225418.json b/tests/real_world_trust_scoring_report_20250723_225418.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225418.json
rename to tests/real_world_trust_scoring_report_20250723_225418.json
diff --git a/real_world_trust_scoring_report_20250723_225548.json b/tests/real_world_trust_scoring_report_20250723_225548.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225548.json
rename to tests/real_world_trust_scoring_report_20250723_225548.json
diff --git a/real_world_trust_scoring_report_20250723_225707.json b/tests/real_world_trust_scoring_report_20250723_225707.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225707.json
rename to tests/real_world_trust_scoring_report_20250723_225707.json
diff --git a/real_world_trust_scoring_report_20250723_225817.json b/tests/real_world_trust_scoring_report_20250723_225817.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225817.json
rename to tests/real_world_trust_scoring_report_20250723_225817.json
diff --git a/real_world_trust_scoring_report_20250723_225831.json b/tests/real_world_trust_scoring_report_20250723_225831.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225831.json
rename to tests/real_world_trust_scoring_report_20250723_225831.json
diff --git a/real_world_trust_scoring_report_20250723_225943.json b/tests/real_world_trust_scoring_report_20250723_225943.json
similarity index 100%
rename from real_world_trust_scoring_report_20250723_225943.json
rename to tests/real_world_trust_scoring_report_20250723_225943.json
diff --git a/test_high_performance_system.py b/tests/test_high_performance_system.py
similarity index 100%
rename from test_high_performance_system.py
rename to tests/test_high_performance_system.py
diff --git a/test_moe_integration.py b/tests/test_moe_integration.py
similarity index 100%
rename from test_moe_integration.py
rename to tests/test_moe_integration.py
diff --git a/test_operation_sindoor_realtime.py b/tests/test_operation_sindoor_realtime.py
similarity index 100%
rename from test_operation_sindoor_realtime.py
rename to tests/test_operation_sindoor_realtime.py
diff --git a/tests/workflow_diagnostic_report_20250726_154015.json b/tests/workflow_diagnostic_report_20250726_154015.json
new file mode 100644
index 0000000..e13e579
--- /dev/null
+++ b/tests/workflow_diagnostic_report_20250726_154015.json
@@ -0,0 +1,265 @@
+{
+  "timestamp": "2025-07-26T15:40:15.501428",
+  "summary": {
+    "total_checks": 13,
+    "passed": 11,
+    "failed": 0,
+    "warnings": 2,
+    "skipped": 0
+  },
+  "results": [
+    {
+      "component": "System Environment",
+      "status": "PASS",
+      "message": "System environment is ready",
+      "duration": 2.6867048740386963,
+      "timestamp": "2025-07-26T15:40:14.096871",
+      "details": {
+        "python_version": "3.12.11",
+        "fastapi_available": true,
+        "uvicorn_available": true,
+        "streamlit_available": true,
+        "pandas_available": true,
+        "numpy_available": true,
+        "plotly_available": true,
+        "requests_available": true,
+        "asyncio_available": true,
+        "logging_available": true,
+        "json_available": true,
+        "memory_available": "6.99 GB",
+        "cpu_count": 4,
+        "disk_free": "2.57 GB",
+        "working_directory": "/app",
+        "project_root": "/app"
+      }
+    },
+    {
+      "component": "Data Uploads",
+      "status": "PASS",
+      "message": "Data uploads system is functional",
+      "duration": 0.0013608932495117188,
+      "timestamp": "2025-07-26T15:40:14.098495",
+      "details": {
+        "uploads_dir_exists": true,
+        "write_permissions": true,
+        "dataset_connector_available": false,
+        "dataset_connector_error": "No module named 'data_engineering'"
+      }
+    },
+    {
+      "component": "Data Engineering",
+      "status": "PASS",
+      "message": "Data engineering components are functional",
+      "duration": 0.012610912322998047,
+      "timestamp": "2025-07-26T15:40:14.111292",
+      "details": {
+        "data_engineering.advanced_trust_scoring_available": false,
+        "data_engineering.advanced_trust_scoring_error": "No module named 'data_engineering'",
+        "data_engineering.cleanlab_integration_available": false,
+        "data_engineering.cleanlab_integration_error": "No module named 'data_engineering'",
+        "data_engineering.trust_scoring_dashboard_available": false,
+        "data_engineering.trust_scoring_dashboard_error": "No module named 'data_engineering'",
+        "data_engineering.data_lifecycle_available": false,
+        "data_engineering.data_lifecycle_error": "No module named 'data_engineering'",
+        "trust_scoring_test": "FAIL",
+        "trust_scoring_error": "No module named 'data_engineering'",
+        "database_connectivity": true
+      }
+    },
+    {
+      "component": "LLM Engineering",
+      "status": "PASS",
+      "message": "LLM engineering components are functional",
+      "duration": 0.000698089599609375,
+      "timestamp": "2025-07-26T15:40:14.112314",
+      "details": {
+        "llm_engineering.llm_lifecycle_available": false,
+        "llm_engineering.llm_lifecycle_error": "No module named 'llm_engineering'",
+        "llm_engineering.providers.base_provider_available": false,
+        "llm_engineering.providers.base_provider_error": "No module named 'llm_engineering'",
+        "llm_engineering.providers.llama_factory_provider_available": false,
+        "llm_engineering.providers.llama_factory_provider_error": "No module named 'llm_engineering'",
+        "llm_lifecycle_manager": "FAIL",
+        "llm_lifecycle_error": "No module named 'llm_engineering'",
+        "llm_config_exists": true
+      }
+    },
+    {
+      "component": "High Performance System",
+      "status": "PASS",
+      "message": "High performance system components are functional",
+      "duration": 0.0009016990661621094,
+      "timestamp": "2025-07-26T15:40:14.113425",
+      "details": {
+        "high_performance_system.core.ultimate_moe_system_available": false,
+        "high_performance_system.core.ultimate_moe_system_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.advanced_expert_ensemble_available": false,
+        "high_performance_system.core.advanced_expert_ensemble_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.intelligent_domain_router_available": false,
+        "high_performance_system.core.intelligent_domain_router_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.enhanced_dataset_profiler_available": false,
+        "high_performance_system.core.enhanced_dataset_profiler_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.comprehensive_pii_detector_available": false,
+        "high_performance_system.core.comprehensive_pii_detector_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.advanced_trust_scorer_available": false,
+        "high_performance_system.core.advanced_trust_scorer_error": "No module named 'high_performance_system'",
+        "moe_system_test": "FAIL",
+        "moe_system_error": "No module named 'high_performance_system'",
+        "expert_ensemble": "FAIL",
+        "expert_ensemble_error": "No module named 'high_performance_system'"
+      }
+    },
+    {
+      "component": "Security System",
+      "status": "PASS",
+      "message": "Security system components are functional",
+      "duration": 0.0003681182861328125,
+      "timestamp": "2025-07-26T15:40:14.113942",
+      "details": {
+        "security.auth_manager_available": false,
+        "security.auth_manager_error": "No module named 'security'",
+        "security.secrets_manager_available": false,
+        "security.secrets_manager_error": "No module named 'security'",
+        "security.security_monitor_available": false,
+        "security.security_monitor_error": "No module named 'security'",
+        "security_webui": "FAIL",
+        "security_webui_error": "No module named 'security'"
+      }
+    },
+    {
+      "component": "MCP Server",
+      "status": "WARNING",
+      "message": "MCP server check completed",
+      "duration": 0.006127595901489258,
+      "timestamp": "2025-07-26T15:40:14.120204",
+      "details": {
+        "mcp_server/server.py_exists": false,
+        "mcp_server/client.py_exists": true,
+        "mcp_server/config.py_exists": true,
+        "mcp_server_running": false,
+        "mcp_server_error": "Server not responding"
+      }
+    },
+    {
+      "component": "Cloud APIs",
+      "status": "PASS",
+      "message": "Cloud APIs check completed",
+      "duration": 0.202164888381958,
+      "timestamp": "2025-07-26T15:40:14.322602",
+      "details": {
+        "cloudscale_apis/docs/cloud_provider_integration.md_exists": true,
+        "cloudscale_apis/endpoints/_exists": true,
+        "cloudscale_apis/webhooks/_exists": true,
+        "aws_sdk_available": true,
+        "azure_sdk_available": false,
+        "gcp_sdk_available": true
+      }
+    },
+    {
+      "component": "Third-party Integrations",
+      "status": "PASS",
+      "message": "Third-party integrations check completed",
+      "duration": 0.0006895065307617188,
+      "timestamp": "2025-07-26T15:40:14.323546",
+      "details": {
+        "thirdparty_integrations.endpoints.verify_realtime_available": false,
+        "thirdparty_integrations.endpoints.verify_realtime_error": "No module named 'thirdparty_integrations'",
+        "thirdparty_integrations.endpoints.verify_batch_available": false,
+        "thirdparty_integrations.endpoints.verify_batch_error": "No module named 'thirdparty_integrations'",
+        "thirdparty_integrations.webhooks.verify_webhook_available": false,
+        "thirdparty_integrations.webhooks.verify_webhook_error": "No module named 'thirdparty_integrations'"
+      }
+    },
+    {
+      "component": "Tests",
+      "status": "WARNING",
+      "message": "Tests check completed",
+      "duration": 1.0560717582702637,
+      "timestamp": "2025-07-26T15:40:15.379817",
+      "details": {
+        "test_high_performance_system.py_exists": true,
+        "simple_unit_test.py_exists": false,
+        "tests/test_advanced_pipeline.py_exists": true,
+        "simple_test_run": "FAIL",
+        "test_error": "ERROR: file or directory not found: simple_unit_test.py\n\n"
+      }
+    },
+    {
+      "component": "Plugins",
+      "status": "PASS",
+      "message": "Plugins check completed",
+      "duration": 0.0003604888916015625,
+      "timestamp": "2025-07-26T15:40:15.380444",
+      "details": {
+        "plugin_loader": "FAIL",
+        "plugin_loader_error": "No module named 'plugins'",
+        "plugins/example_plugin.py_exists": true,
+        "plugins/hallucination_detector.py_exists": true,
+        "plugins/eu_gdpr_embed.py_exists": true
+      }
+    },
+    {
+      "component": "Analytics & Dashboards",
+      "status": "PASS",
+      "message": "Analytics and dashboards check completed",
+      "duration": 0.0002505779266357422,
+      "timestamp": "2025-07-26T15:40:15.380855",
+      "details": {
+        "high_performance_system/analytics/ultimate_analytics_dashboard.py_exists": true,
+        "high_performance_system/analytics/sme_dashboard.py_exists": true,
+        "data_engineering/trust_scoring_dashboard.py_exists": true,
+        "operation_sindoor_dashboard.py_exists": false,
+        "ultimate_dashboard": "FAIL",
+        "ultimate_dashboard_error": "No module named 'high_performance_system'"
+      }
+    },
+    {
+      "component": "Production Server",
+      "status": "PASS",
+      "message": "Production server check completed",
+      "duration": 0.11996245384216309,
+      "timestamp": "2025-07-26T15:40:15.501130",
+      "details": {
+        "superfast_production_server.py_exists": true,
+        "ote_api.py_exists": false,
+        "production_server_running": true,
+        "production_server_health": {
+          "status": "healthy",
+          "timestamp": "2025-07-26T15:40:15.458071",
+          "uptime": 48819.97430515289,
+          "cache_stats": {
+            "hits": 0,
+            "misses": 0
+          },
+          "system_ready": true
+        },
+        "performance_endpoint": "PASS",
+        "performance_data": {
+          "performance_stats": {
+            "total_requests": 0,
+            "total_latency": 0.0,
+            "avg_latency": 0.0,
+            "cache_hit_rate": 0.0,
+            "error_rate": 0.0
+          },
+          "cache_stats": {
+            "hits": 0,
+            "misses": 0
+          },
+          "cache_size": 0,
+          "timestamp": "2025-07-26T15:40:15.497127"
+        }
+      }
+    }
+  ],
+  "recommendations": [
+    {
+      "priority": "MEDIUM",
+      "action": "Address warnings",
+      "components": [
+        "MCP Server",
+        "Tests"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/workflow_diagnostic_report_20250726_020600.json b/workflow_diagnostic_report_20250726_020600.json
new file mode 100644
index 0000000..4c91acf
--- /dev/null
+++ b/workflow_diagnostic_report_20250726_020600.json
@@ -0,0 +1,242 @@
+{
+  "timestamp": "2025-07-26T02:06:00.806086",
+  "summary": {
+    "total_checks": 13,
+    "passed": 10,
+    "failed": 0,
+    "warnings": 3,
+    "skipped": 0
+  },
+  "results": [
+    {
+      "component": "System Environment",
+      "status": "PASS",
+      "message": "System environment is ready",
+      "duration": 1.7772915363311768,
+      "timestamp": "2025-07-26T02:05:59.214582",
+      "details": {
+        "python_version": "3.12.11",
+        "fastapi_available": true,
+        "uvicorn_available": true,
+        "streamlit_available": true,
+        "pandas_available": true,
+        "numpy_available": true,
+        "plotly_available": true,
+        "requests_available": true,
+        "asyncio_available": true,
+        "logging_available": true,
+        "json_available": true,
+        "memory_available": "7.16 GB",
+        "cpu_count": 4,
+        "disk_free": "2.58 GB",
+        "working_directory": "/app",
+        "project_root": "/app"
+      }
+    },
+    {
+      "component": "Data Uploads",
+      "status": "PASS",
+      "message": "Data uploads system is functional",
+      "duration": 0.014145374298095703,
+      "timestamp": "2025-07-26T02:05:59.229098",
+      "details": {
+        "uploads_dir_exists": true,
+        "write_permissions": true,
+        "dataset_connector_available": false,
+        "dataset_connector_error": "No module named 'data_engineering'"
+      }
+    },
+    {
+      "component": "Data Engineering",
+      "status": "PASS",
+      "message": "Data engineering components are functional",
+      "duration": 0.12368917465209961,
+      "timestamp": "2025-07-26T02:05:59.353004",
+      "details": {
+        "data_engineering.advanced_trust_scoring_available": false,
+        "data_engineering.advanced_trust_scoring_error": "No module named 'data_engineering'",
+        "data_engineering.cleanlab_integration_available": false,
+        "data_engineering.cleanlab_integration_error": "No module named 'data_engineering'",
+        "data_engineering.trust_scoring_dashboard_available": false,
+        "data_engineering.trust_scoring_dashboard_error": "No module named 'data_engineering'",
+        "data_engineering.data_lifecycle_available": false,
+        "data_engineering.data_lifecycle_error": "No module named 'data_engineering'",
+        "trust_scoring_test": "FAIL",
+        "trust_scoring_error": "No module named 'data_engineering'",
+        "database_connectivity": true
+      }
+    },
+    {
+      "component": "LLM Engineering",
+      "status": "PASS",
+      "message": "LLM engineering components are functional",
+      "duration": 0.0006618499755859375,
+      "timestamp": "2025-07-26T02:05:59.353965",
+      "details": {
+        "llm_engineering.llm_lifecycle_available": false,
+        "llm_engineering.llm_lifecycle_error": "No module named 'llm_engineering'",
+        "llm_engineering.providers.base_provider_available": false,
+        "llm_engineering.providers.base_provider_error": "No module named 'llm_engineering'",
+        "llm_engineering.providers.llama_factory_provider_available": false,
+        "llm_engineering.providers.llama_factory_provider_error": "No module named 'llm_engineering'",
+        "llm_lifecycle_manager": "FAIL",
+        "llm_lifecycle_error": "No module named 'llm_engineering'",
+        "llm_config_exists": true
+      }
+    },
+    {
+      "component": "High Performance System",
+      "status": "PASS",
+      "message": "High performance system components are functional",
+      "duration": 0.0008711814880371094,
+      "timestamp": "2025-07-26T02:05:59.355021",
+      "details": {
+        "high_performance_system.core.ultimate_moe_system_available": false,
+        "high_performance_system.core.ultimate_moe_system_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.advanced_expert_ensemble_available": false,
+        "high_performance_system.core.advanced_expert_ensemble_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.intelligent_domain_router_available": false,
+        "high_performance_system.core.intelligent_domain_router_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.enhanced_dataset_profiler_available": false,
+        "high_performance_system.core.enhanced_dataset_profiler_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.comprehensive_pii_detector_available": false,
+        "high_performance_system.core.comprehensive_pii_detector_error": "No module named 'high_performance_system'",
+        "high_performance_system.core.advanced_trust_scorer_available": false,
+        "high_performance_system.core.advanced_trust_scorer_error": "No module named 'high_performance_system'",
+        "moe_system_test": "FAIL",
+        "moe_system_error": "No module named 'high_performance_system'",
+        "expert_ensemble": "FAIL",
+        "expert_ensemble_error": "No module named 'high_performance_system'"
+      }
+    },
+    {
+      "component": "Security System",
+      "status": "PASS",
+      "message": "Security system components are functional",
+      "duration": 0.00035881996154785156,
+      "timestamp": "2025-07-26T02:05:59.355529",
+      "details": {
+        "security.auth_manager_available": false,
+        "security.auth_manager_error": "No module named 'security'",
+        "security.secrets_manager_available": false,
+        "security.secrets_manager_error": "No module named 'security'",
+        "security.security_monitor_available": false,
+        "security.security_monitor_error": "No module named 'security'",
+        "security_webui": "FAIL",
+        "security_webui_error": "No module named 'security'"
+      }
+    },
+    {
+      "component": "MCP Server",
+      "status": "WARNING",
+      "message": "MCP server check completed",
+      "duration": 0.007213115692138672,
+      "timestamp": "2025-07-26T02:05:59.362933",
+      "details": {
+        "mcp_server/server.py_exists": false,
+        "mcp_server/client.py_exists": true,
+        "mcp_server/config.py_exists": true,
+        "mcp_server_running": false,
+        "mcp_server_error": "Server not responding"
+      }
+    },
+    {
+      "component": "Cloud APIs",
+      "status": "PASS",
+      "message": "Cloud APIs check completed",
+      "duration": 0.23957180976867676,
+      "timestamp": "2025-07-26T02:05:59.602769",
+      "details": {
+        "cloudscale_apis/docs/cloud_provider_integration.md_exists": true,
+        "cloudscale_apis/endpoints/_exists": true,
+        "cloudscale_apis/webhooks/_exists": true,
+        "aws_sdk_available": true,
+        "azure_sdk_available": false,
+        "gcp_sdk_available": true
+      }
+    },
+    {
+      "component": "Third-party Integrations",
+      "status": "PASS",
+      "message": "Third-party integrations check completed",
+      "duration": 0.0004413127899169922,
+      "timestamp": "2025-07-26T02:05:59.603562",
+      "details": {
+        "thirdparty_integrations.endpoints.verify_realtime_available": false,
+        "thirdparty_integrations.endpoints.verify_realtime_error": "No module named 'thirdparty_integrations'",
+        "thirdparty_integrations.endpoints.verify_batch_available": false,
+        "thirdparty_integrations.endpoints.verify_batch_error": "No module named 'thirdparty_integrations'",
+        "thirdparty_integrations.webhooks.verify_webhook_available": false,
+        "thirdparty_integrations.webhooks.verify_webhook_error": "No module named 'thirdparty_integrations'"
+      }
+    },
+    {
+      "component": "Tests",
+      "status": "WARNING",
+      "message": "Tests check completed",
+      "duration": 1.1941814422607422,
+      "timestamp": "2025-07-26T02:06:00.797914",
+      "details": {
+        "test_high_performance_system.py_exists": true,
+        "simple_unit_test.py_exists": false,
+        "tests/test_advanced_pipeline.py_exists": true,
+        "simple_test_run": "FAIL",
+        "test_error": "ERROR: file or directory not found: simple_unit_test.py\n\n"
+      }
+    },
+    {
+      "component": "Plugins",
+      "status": "PASS",
+      "message": "Plugins check completed",
+      "duration": 0.0003821849822998047,
+      "timestamp": "2025-07-26T02:06:00.798560",
+      "details": {
+        "plugin_loader": "FAIL",
+        "plugin_loader_error": "No module named 'plugins'",
+        "plugins/example_plugin.py_exists": true,
+        "plugins/hallucination_detector.py_exists": true,
+        "plugins/eu_gdpr_embed.py_exists": true
+      }
+    },
+    {
+      "component": "Analytics & Dashboards",
+      "status": "PASS",
+      "message": "Analytics and dashboards check completed",
+      "duration": 0.0002613067626953125,
+      "timestamp": "2025-07-26T02:06:00.798988",
+      "details": {
+        "high_performance_system/analytics/ultimate_analytics_dashboard.py_exists": true,
+        "high_performance_system/analytics/sme_dashboard.py_exists": true,
+        "data_engineering/trust_scoring_dashboard.py_exists": true,
+        "operation_sindoor_dashboard.py_exists": false,
+        "ultimate_dashboard": "FAIL",
+        "ultimate_dashboard_error": "No module named 'high_performance_system'"
+      }
+    },
+    {
+      "component": "Production Server",
+      "status": "WARNING",
+      "message": "Production server check completed",
+      "duration": 0.006836891174316406,
+      "timestamp": "2025-07-26T02:06:00.805957",
+      "details": {
+        "superfast_production_server.py_exists": true,
+        "ote_api.py_exists": false,
+        "production_server_running": false,
+        "production_server_error": "Server not responding",
+        "performance_endpoint": "FAIL"
+      }
+    }
+  ],
+  "recommendations": [
+    {
+      "priority": "MEDIUM",
+      "action": "Address warnings",
+      "components": [
+        "MCP Server",
+        "Tests",
+        "Production Server"
+      ]
+    }
+  ]
+}
\ No newline at end of file