To implement action capabilities similar to those described in the Writer.com blog post about Palmyra X 004 and to improve the user experience for security threat analysts, we need to enhance the SecuStreamAI project. Here's an updated overview of our approach:
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CLI Tool Development: Create a command-line interface (CLI) tool that interacts with the SecuStreamAI API, allowing analysts to perform common tasks quickly without switching between tabs or manually crafting curl commands.
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Real-time Event Streaming: Implement a WebSocket connection to receive real-time updates on new security events and their initial analysis results.
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Enhanced Query Capabilities: Develop a simple query language that allows analysts to filter and analyze events more efficiently.
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Automated Routine Tasks: Create scripts or scheduled jobs that perform regular checks and generate reports automatically.
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Tool Calling Framework: Implement a dynamic tool calling system that allows the AI to decide which tools to use based on the input and context. This would involve creating a registry of available tools and their functions.
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Action Execution Engine: Develop an engine that can execute the actions determined by the AI across various systems and tools.
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Expand the Adaptive Hybrid Analyzer: Enhance the existing Adaptive Hybrid Analyzer to include decision-making capabilities for tool selection and action execution.
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Graph-based RAG Integration: Implement a graph database (e.g., Neo4j) to store and retrieve contextual information, replacing or augmenting the current PostgreSQL setup.
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Code Generation and Deployment: Add capabilities for the AI to generate, test, and deploy code changes automatically.
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Structured Output Generation: Implement a system for generating structured outputs (e.g., JSON, XML) for easier integration with other systems.
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Expand API Integrations: Develop integrations with various enterprise tools and systems (e.g., CRM, SIEM, ticketing systems).
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Enhanced Natural Language Processing: Improve the NLP capabilities to better understand complex queries and translate them into actionable steps.
These enhancements would transform SecuStreamAI into a more advanced, AI-driven security operations platform with the following capabilities:
- Streamlined Analyst Workflow: Security analysts can interact with the system efficiently using a CLI tool and real-time event streaming.
- Automated Workflow Execution: The system can automatically perform complex, multi-step security operations without human intervention.
- Dynamic Tool Integration: It would be able to interact with a wide range of security and IT tools, choosing the most appropriate ones for each task.
- Intelligent Decision Making: The enhanced Adaptive Hybrid Analyzer would make sophisticated decisions about how to handle security events.
- Code-level Adaptability: The system could modify its own codebase to adapt to new security threats or operational needs.
- Contextual Understanding: With graph-based RAG, it would have a deeper understanding of the relationships between different security events and entities.
- Natural Language Interaction: Security analysts could interact with the system using natural language queries to investigate issues or initiate actions.
To start implementing these features, we should begin by:
- Developing the CLI tool (
secustreamai
) using Python with Click or Typer. - Implementing a WebSocket endpoint for real-time event streaming using FastAPI's WebSocket support.
- Enhancing the API to support more complex queries and filtering.
- Creating new endpoints or modifying existing ones to support the proposed functionality.
- Updating the
architecture.md
file to reflect these new components and capabilities.
This enhanced project would position SecuStreamAI as a cutting-edge, AI-driven security operations platform, capable of autonomous decision-making and action across a wide range of security scenarios, while providing a more efficient and user-friendly experience for security analysts.