Practical guidance for ML Engineers, Data Scientists, and Data Engineers
Warning : This guide provides educational information about GDPR compliance from a technical perspective. It is not legal advice. Always consult with qualified legal counsel for your specific situation. The authors and contributors are not responsible for any compliance decisions made based on this content.
This guide is specifically designed for technical professionals working with data:
- Machine Learning Engineers building and deploying models that process personal data
- Data Scientists conducting analysis on datasets containing personal information
- Data Engineers designing and maintaining data pipelines, warehouses, and lakes
- Software Developers creating applications that collect or process user data
- DevOps & Infrastructure Engineers responsible for data storage and processing systems
- Security Engineers implementing data protection measures
- Technical Leads & Architects making decisions about data collection and system design
This guide is structured to help you:
- Understand GDPR requirements from a technical perspective
- Implement compliant systems with practical guidance
- Navigate common challenges faced by data professionals
- Access tools and templates to accelerate compliance work
Important Notes:
- This guide focuses on technical implementation, not legal interpretation
- Requirements may vary based on your specific context and jurisdiction
- Always supplement with official regulatory guidance and legal advice
GDPR operates on seven core principles that guide all processing of personal data. As a technical professional, you need to translate these principles into system design and implementation decisions.
The Seven Principles:
- Lawfulness, Fairness, and Transparency – Processing must have a legal basis, be fair to individuals, and be transparent about how data is used
- Purpose Limitation – Data can only be collected for specified, explicit, and legitimate purposes
- Data Minimization – Only collect data that is adequate, relevant, and necessary
- Accuracy – Keep personal data accurate and up-to-date
- Storage Limitation – Don't keep data longer than necessary for its purpose
- Integrity and Confidentiality – Process data securely and protect against unauthorized access
- Accountability – Be able to demonstrate compliance with all principles
GDPR applies to organizations that:
- Process personal data in the context of an establishment in the EU – Regardless of where the processing takes place
- Offer goods or services to individuals in the EU – Even if the organization is based outside the EU
- Monitor the behavior of individuals in the EU – Including tracking, profiling, or analyzing behavior
Key Indicators of Targeting EU Individuals:
- Website/app available in EU languages
- Prices displayed in EUR or other EU currencies
- Marketing or advertising directed at EU audiences
- .eu domain or EU country-specific domains
- Mentions of serving EU customers or users
Territorial Scope Dicision Tree
flowchart TD
A[Does GDPR Apply?] --> B{Company Establishment?}
B -->|Yes, in EU| C[GDPR APPLIES]
B -->|No| D{Target EU Individuals?}
D -->|Yes| E[Offering Goods/Services?]
E -->|Yes| C
E -->|No| F[Monitoring Behavior?]
F -->|Yes| C
F -->|No| G[GDPR MAY NOT APPLY]
D -->|No| G
subgraph "Indicators of Targeting EU"
H[EU Languages]
I[EUR Pricing]
J[EU Marketing]
K[.eu Domains]
L[EU References]
end
E -.-> H
E -.-> I
E -.-> J
F -.-> K
F -.-> L
Data Controller – Determines the purposes and means of processing personal data. As a controller, you must ensure compliance with GDPR principles, establish lawful bases for processing, and handle data subject rights.
Data Processor – Processes personal data on behalf of a controller. As a processor, you must follow controller instructions, implement appropriate security measures, assist controllers with compliance, and notify them of data breaches.
Joint Controllership – When multiple parties jointly determine processing purposes and means.
- Requires: Clear agreement on responsibilities (Article 26)
- Examples: Platform and app developers, research collaborations, partnership projects
- Technical consideration: Design systems with clear data ownership and access boundaries
Practical Implications for Technical Teams:
- Controllers must design systems for compliance from the start
- Processors must implement security and follow documented instructions
- Both must maintain proper documentation and audit trails
Definitions and Responsibilities
| Aspect | Data Controller | Data Processor |
|---|---|---|
| Definition | Determines the purposes and means of processing personal data | Processes personal data on behalf of the controller |
| Primary Responsibility | Compliance with GDPR principles, lawful basis, data subject rights | Security, following controller instructions, assisting controller |
| Technical Implications | Design systems for compliance, implement rights mechanisms | Implement security measures, processing safeguards |
| Liability | Primary responsibility for compliance | Liable for breaches of processor obligations |
| Example | Company developing customer analytics platform | Cloud provider hosting the data, analytics service provider |
Technical Decision Tree
graph TD
A[Are you determining<br>why and how data is processed?] -->|Yes| B[You are likely a Controller]
A -->|No| C[Are you processing data<br>on someone else's instructions?]
C -->|Yes| D[You are likely a Processor]
C -->|No| E[Joint Controller situation possible]
B --> F[Controller Responsibilities]
F --> F1[Determine lawful basis]
F --> F2[Implement data subject rights]
F --> F3[Conduct DPIAs when needed]
F --> F4[Appoint DPO if required]
D --> G[Processor Responsibilities]
G --> G1[Follow controller instructions]
G --> G2[Implement appropriate security]
G --> G3[Assist controller with compliance]
G --> G4[Notify controller of breaches]
Every processing activity must have one of these lawful bases:
- Consent – Individual has given clear, affirmative consent for specific processing
- Contract – Processing is necessary for a contract with the individual
- Legal Obligation – Processing is required by EU or member state law
- Vital Interests – Processing is necessary to protect someone's life
- Public Task – Processing is necessary for public interest or official authority
- Legitimate Interests – Processing is necessary for your legitimate interests (except where overridden by individual's rights)
Special Categories of Data (sensitive data like health information) require additional conditions and stronger safeguards.
Technical Implementation Requirements:
- Document the lawful basis for each processing activity
- Implement mechanisms to track and manage consent where used
- For legitimate interests, conduct and document a balancing test
- Implement stronger security measures for sensitive data
Data Minimization Strategies:
- Schema Design – Define strict schemas that only include necessary fields
- Input Validation – Reject unnecessary data at ingestion points
- Feature Selection – In ML contexts, select only relevant features
- Sampling – Use statistical sampling instead of full datasets when possible
- Purpose Tagging – Tag each data element with its specific purpose
Collection Best Practices:
- Define Clear Purposes – Document exactly why each data element is collected
- Implement Progressive Disclosure – Collect additional information only as needed
- Use Default Null Values – Don't require fields that aren't essential
- Regular Review – Periodically review collected data and remove unnecessary elements
Storage Security Requirements:
- Encryption at Rest – Use strong encryption for stored personal data
- Encryption in Transit – Use TLS 1.2+ for data transmission
- Access Controls – Implement role-based access control (RBAC)
- Audit Logging – Maintain immutable logs of data access and changes
- Secure Backups – Encrypt backups and control access to them
Retention Policy Implementation:
- Define Retention Periods – Based on purpose, legal requirements, and business needs
- Automate Deletion – Implement scheduled deletion of expired data
- Archive Management – Separate active and archived data with different access controls
- Exception Handling – Process for legal holds and data preservation orders
| Technique | Use Case | GDPR Impact |
|---|---|---|
| Anonymization | Analytics, Reporting | Data no longer personal |
| Pseudonymization | ML Training, Testing | Reduced risk level |
| Aggregation | Business Intelligence | Often not personal data |
| Differential Privacy | Statistical analysis | Strong mathematical guarantees |
Available PETs for Technical Implementation:
- Pseudonymization – Replace identifying fields with artificial identifiers
- Anonymization – Irreversibly remove identifying information
- Differential Privacy – Add statistical noise to protect individual data points
- Homomorphic Encryption – Perform computations on encrypted data
- Federated Learning – Train models without centralizing training data
- Secure Multi-Party Computation – Joint analysis without sharing raw data
Choosing Appropriate PETs:
- Consider the sensitivity of the data
- Evaluate computational requirements and performance impact
- Assess the level of protection needed
- Consider interoperability with existing systems
Legal Mechanisms for Transferring Data Outside the EU:
- Adequacy Decisions – Countries deemed to provide adequate protection (e.g., UK, Japan)
- Standard Contractual Clauses (SCCs) – EU-approved contract terms
- Binding Corporate Rules (BCRs) – For multinational organizations
- Derogations – Specific situations like explicit consent or contract necessity
Technical Considerations for International Transfers:
- Implement data residency controls in cloud configurations
- Use encryption for data in transit between regions
- Maintain documentation of transfer mechanisms
- Conduct regular reviews of third-party data processors
- Implement additional safeguards for high-risk transfers
International Data Transfer Mechanisms
graph TB
classDef default fill:#f9f9f9,color:#333
A[EU Personal Data] --> B{Transfer Destination?}
B -->|EU/EEA| C[Free Flow]
B -->|Adequate Country| D[Adequacy Decision<br>e.g., UK, Japan]
B -->|Third Country| E{Safeguards Required}
E --> F[Standard Contractual Clauses<br>SCCs]
E --> G[Binding Corporate Rules<br>BCRs]
E --> H[Derogations<br>Consent, Contract, etc.]
F --> I[Additional Measures<br>Encryption, Access Controls]
G --> I
H --> I
I --> J[Lawful Transfer]
style C fill:#4caf50
style D fill:#2196f3
style F fill:#ff9800
style G fill:#f57c00
style H fill:#e91e63
Training Data Requirements:
- Lawful Basis – Must have a lawful basis for using personal data in training
- Data Provenance – Document sources and collection methods
- Bias Assessment – Evaluate training data for representational bias
- Purpose Alignment – Ensure training purpose aligns with original collection purposes
- Third-Party Data – Verify compliance of purchased or licensed datasets
Special Considerations:
- Publicly Available Data – Still subject to GDPR if contains personal data
- Web Scraping – Requires careful legal assessment and potentially consent
- Synthetic Data – Can reduce compliance burden but requires validation
Article 22 applies when:
- Decisions are based solely on automated processing
- Decisions produce legal effects or similarly significant effects on individuals
Requirements for Automated Decision-Making:
| Requirement | What it asks | Technical Implementation |
|---|
- Right to Human Intervention | Individuals can request human review |
- Right to Explanation | Ability to understand how decisions are made |Provide clear communication about automated processing
- Right to Contest | Challenge automated decisions |
- Safeguards | Implement measures to ensure fairness and prevent errors| Regular accuracy testing
- Credit scoring or loan approvals
- Recruitment or candidate evaluation
- Insurance underwriting or pricing
- Any decision with legal or significant effects
Technical Implementation:
- Design systems with human review workflows
- Implement model explainability features
- Create audit trails for automated decisions
Bias Detection and Mitigation:
- Pre-processing – Balance training datasets across demographic groups
- In-processing – Incorporate fairness constraints during model training
- Post-processing – Adjust model outputs to ensure fairness
- Continuous Monitoring – Track model performance across different groups
Explainability Requirements:
- Model Documentation – Create detailed model cards
- Feature Importance – Identify which features drive predictions
- Counterfactual Explanations – Show what would change a decision
- User-Friendly Explanations – Present explanations in understandable terms
Technical Approaches:
- Federated Learning – Train models on decentralized devices without sharing raw data
- Differential Privacy – Add calibrated noise to protect individual contributions
- Secure Aggregation – Combine model updates without exposing individual data
- Synthetic Data Generation – Create artificial datasets that preserve statistical properties
- Homomorphic Encryption – Perform computations on encrypted data
Implementation Considerations:
- Balance privacy protection with model accuracy
- Consider computational overhead of privacy techniques
- Evaluate compatibility with existing ML pipelines
- Document privacy guarantees and limitations
# Example: Compliant ML Pipeline Checklist
compliant_ml_pipeline = {
"data_sourcing": [
"Verify lawful basis for training data",
"Document data lineage and provenance",
"Check third-party data compliance"
],
"model_development": [
"Implement bias and fairness testing",
"Create model cards with documentation",
"Consider privacy-preserving ML techniques",
"Document feature engineering decisions"
],
"deployment": [
"Enable human review for significant decisions",
"Implement model explainability",
"Set up monitoring for model drift",
"Plan for model retraining with data deletions"
]
}| Right | Technical Implementation | Complexity |
|---|---|---|
| Access | Single view of all user data across systems | High |
| Erasure | Complete deletion from all systems + backups | Very High |
| Portability | Structured export (JSON, CSV, XML) | Medium |
| Rectification | Data correction propagation | High |
| Objection | Processing opt-out mechanisms | Medium |
graph TD
A[Data Subject Request] --> B{Erasure Request?}
B -->|Yes| C[Delete from DB]
C --> D[Update ML Models?]
D --> E{Model Type}
E -->|Batch| F[Schedule Retraining]
E -->|Online| G[Update Incrementally]
D --> H[Update Inference data]
B -->|Portability| I[Export data + explanations]
System Design Requirements:
- Centralized Request Management – Single entry point for all rights requests
- Distributed Processing – Systems to locate data across all storage locations
- Workflow Automation – Automated processing of common request types
- Status Tracking – Monitor request progress and completion
- Verification Systems – Confirm requestor identity without collecting additional data
Key Technical Components:
- Request API – Standardized interface for submitting rights requests
- Data Discovery Service – Locate personal data across systems
- Action Engine – Execute rights actions (delete, export, etc.)
- Audit System – Log all rights-related activities
- Notification System – Communicate with requestors
Access Rights (Article 15):
- Provide confirmation of whether data is being processed
- Give access to the personal data being processed
- Provide additional information about processing
Portability Rights (Article 20):
- Receive data in structured, commonly used, machine-readable format
- Have data transmitted directly to another controller where technically feasible
- Applies to data provided by the individual or observed through their use of service
Technical Implementation:
- Create comprehensive user data profiles from all systems
- Implement data export in multiple formats (JSON, CSV, XML)
- Ensure exports include all relevant metadata
- Maintain data format consistency for portability
- Implement secure transmission mechanisms for data transfers
When Erasure Applies:
- Data is no longer necessary for its original purpose
- Individual withdraws consent and no other lawful basis exists
- Individual objects to processing and no overriding legitimate grounds exist
- Data has been unlawfully processed
- Legal obligation requires deletion
Technical Challenges:
- Distributed Data – Data stored across multiple systems and databases
- Backups – Personal data in backup systems and archives
- Derived Data – Data created through processing (analytics, ML models)
- Shared Data – Data shared with third parties or processors
- Logs – Audit logs and system logs containing personal data
ML Model Implications:
- Model Retraining – Whether to retrain models after data deletion
- Influence Removal – Techniques to remove individual's influence from models
- Model Versioning – Track which data was used for each model version
- Synthetic Replacement – Replace deleted data with synthetic equivalents
Common Implementation Patterns:
-
Batch Processing Pattern
- Collect requests throughout the day
- Process in scheduled batches
- Update status asynchronously
- Suitable for non-urgent requests
-
Real-time Processing Pattern
- Process requests immediately
- Provide instant confirmation
- Requires robust error handling
- Suitable for simple requests
-
Hybrid Approach
- Simple requests handled in real-time
- Complex requests processed asynchronously
- Status tracking for all requests
- Escalation for delayed processing
Request Verification:
- Use existing authentication mechanisms when possible
- Implement challenge-response for unauthenticated requests
- Avoid collecting additional personal data for verification
- Set appropriate security levels based on request sensitivity
| Layer | Implementation |
|---|---|
| Application | Data minimization, purpose limitation at API level |
| Processing | Pseudonymization, aggregation for analytics |
| Storage | Encryption, access controls, retention policies |
| Sharing | Anonymization, data masking, secure data sharing |
- Federated Learning: Train models without centralizing data
- Homomorphic Encryption: Compute on encrypted data
- Secure Multi-Party Computation: Joint analysis without sharing raw data
- Synthetic Data Generation: Create realistic but artificial datasets
Privacy-First Architecture Principles:
- Data Localization – Process data close to its source when possible
- Minimal Data Movement – Reduce unnecessary data transfers
- Explicit Consent Flows – Design clear consent collection points
- Privacy Gateways – Implement centralized privacy controls
- Data Lifecycle Management – Built-in retention and deletion
Microservices Privacy Patterns:
- Privacy Service – Centralized service handling all privacy functions
- Data Tagging – Metadata indicating sensitivity and retention requirements
- Privacy-Aware API Gateway – Enforce privacy policies at API boundaries
- Event-Driven Privacy – Publish privacy events for distributed processing
When DPIAs Are Required:
- Systematic and extensive profiling with significant effects
- Large-scale processing of sensitive data
- Systematic monitoring of publicly accessible areas
- Innovative use of new technologies
- Automated decision-making with legal/significant effects
DPIA Process for Technical Teams:
- Describe Processing – Document data flows, systems, and purposes
- Assess Necessity and Proportionality – Evaluate if processing is justified
- Identify Risks – Technical, organizational, and individual risks
- Propose Measures – Technical and organizational safeguards
- Document Outcomes – Record assessment and mitigation plans
Technical Risk Assessment Areas:
- Data security vulnerabilities
- System reliability and error rates
- Data accuracy and quality controls
- Access control effectiveness
- Data breach detection capabilities
Security Requirements for Personal Data:
- Confidentiality – Protection against unauthorized access
- Integrity – Protection against unauthorized modification
- Availability – Ensure accessibility when needed
- Resilience – Ability to recover from incidents
- Continuous Protection – Security throughout data lifecycle
Technical Security Measures:
- Encryption – Strong encryption for data at rest and in transit
- Access Controls – Role-based access with least privilege principle
- Audit Logging – Comprehensive, immutable audit trails
- Input Validation – Protect against injection and manipulation attacks
- Secure Development – Follow security best practices in development
Privacy by Default Requirements:
- Highest privacy settings automatically applied
- Minimal data collection by default
- No pre-selected consent options
- Easy-to-use privacy controls
- Clear privacy information at point of collection
Implementation Guidelines:
- Design user interfaces with privacy as the default state
- Implement privacy-preserving defaults in system configurations
- Provide clear options to increase privacy (not decrease it)
- Ensure privacy settings are persistent and respected
- Regularly review and update default settings
Required Documentation:
- Name and contact details of controller/processor
- Purposes of the processing
- Categories of data subjects and personal data
- Categories of recipients of personal data
- Transfers to third countries and safeguards
- Retention periods for different data categories
- Technical and organizational security measures
Technical Implementation Tips:
- Automate documentation generation where possible
- Integrate with existing system documentation
- Maintain version control for documentation
- Link technical documentation to legal requirements
- Regular review and update cycles
Data Mapping Components:
- Data Inventory – Catalog of all data stores containing personal data
- Flow Diagrams – Visual representations of data movement
- Processing Descriptions – Detailed descriptions of each processing activity
- System Dependencies – Relationships between systems and data flows
- Third-Party Connections – External systems and services handling data
Data Lineage Tracking:
- Source of each data element
- Transformations applied to data
- Systems that process the data
- Final destinations and uses
- Retention and deletion triggers
Vendor Assessment Process:
- Pre-engagement Assessment – Evaluate vendor privacy capabilities
- Contractual Requirements – Include GDPR obligations in contracts
- Ongoing Monitoring – Regular reviews of vendor compliance
- Termination Planning – Procedures for ending vendor relationships
Technical Due Diligence Areas:
- Security certifications and audit reports
- Data processing locations and transfers
- Subprocessor management
- Incident response capabilities
- Data deletion and return procedures
Available Templates:
- Data Processing Agreement (DPA) template
- Data Protection Impact Assessment (DPIA) template
- Records of Processing Activities (ROPA) template
- Data Breach Response Plan template
- Vendor Assessment Questionnaire
- Privacy Notice template
- Consent Language templates
- Data Subject Request forms
timeline
title Data Breach Response Timeline
section Discovery (T+0 hours)
Detect breach : Notify DPO/security team
section Assessment (T+0-24 hours)
Contain breach : Assess scope & impact
Document details : Determine affected individuals
section Notification (T+24-72 hours)
Notify authorities : Prepare user notifications
Implement remediation : Update security controls
section Post-Incident (T+72+ hours)
Complete investigation : Update policies
Review lessons learned : Retrain staff if needed
Challenge 1: Legacy System Integration
- Problem: Existing systems not designed with privacy in mind
- Solution: Implement privacy wrapper layers, gradual refactoring, data minimization at integration points
- Technical Approach: API gateways with privacy controls, data anonymization proxies, gradual migration to privacy-aware architectures
Challenge 2: ML Model Compliance
- Problem: Training models with personal data without clear lawful basis
- Solution: Implement privacy-preserving ML techniques, document data provenance, conduct DPIA
- Technical Approach: Federated learning, differential privacy, synthetic data generation, model explainability
Challenge 3: Cross-Border Data Flows
- Problem: Global teams needing access to EU personal data
- Solution: Implement data residency controls, use SCCs, apply additional safeguards
- Technical Approach: Data localization policies, encrypted data transfers, access controls based on location
Example 1: E-commerce Personalization
- Situation: Online retailer using purchase history for recommendations
- GDPR Considerations: Lawful basis (legitimate interests), data minimization, user rights
- Technical Implementation: Pseudonymized analytics, clear opt-out mechanisms, regular data purging, transparent algorithms
Example 2: Healthcare Analytics
- Situation: Hospital system analyzing patient data for research
- GDPR Considerations: Special category data, explicit consent, additional safeguards
- Technical Implementation: Strong encryption, strict access controls, data anonymization, ethics committee approval tracking
Example 3: SaaS Platform
- Situation: Cloud service processing customer data globally
- GDPR Considerations: Controller/processor relationships, international transfers, security measures
- Technical Implementation: Data processing agreements, SCCs, security certifications, breach notification systems
Q: Do we need to delete data from ML models when users request erasure? A: It depends on the situation. For models where individuals can be re-identified from the model itself, you may need to retrain or modify the model. Document your approach and consider privacy-preserving techniques that minimize this issue.
Q: Can we use publicly available data for training without consent? A: Publicly available personal data is still subject to GDPR. You need a lawful basis for processing it, and should consider the individual's reasonable expectations. Legitimate interests may apply but requires a balancing test.
Q: How do we handle data subject requests in microservices architectures? A: Implement a centralized request service that coordinates with all microservices. Each service should expose an interface for locating and processing personal data. Use event-driven patterns for coordination.
Q: What encryption standards should we use for GDPR compliance? A: Use industry-standard encryption (AES-256 for data at rest, TLS 1.2+ for data in transit). The specific requirements depend on the sensitivity of the data and the risks involved. Document your encryption practices.
Q: How often should we review our data retention periods? A: Conduct annual reviews of all retention periods. Also review whenever there are changes to processing purposes, legal requirements, or business needs. Automated systems should flag data approaching retention limits.
European Data Protection Board (EDPB):
- Guidelines on concepts of controller and processor
- Recommendations on supplementary measures for international transfers
- Guidelines on Data Protection Impact Assessment
- Guidance on consent under Regulation 2016/679
National Data Protection Authorities:
- UK: Information Commissioner's Office (ICO) guidance
- France: Commission Nationale de l'Informatique et des Libertés (CNIL)
- Germany: Conference of Independent Data Protection Authorities
- Ireland: Data Protection Commission (DPC)
Key Documents:
- GDPR Text (Regulation (EU) 2016/679)
- EDPB Binding Decisions
- Court of Justice of the European Union (CJEU) rulings
- National implementation laws and guidance
# 1. Privacy Assessment
- [ ] Identify data types and sensitivity
- [ ] Determine lawful basis for processing
- [ ] Check if DPIA is required
# 2. Technical Design
- [ ] Apply data minimization principles
- [ ] Design for data subject rights
- [ ] Plan data retention and deletion
# 3. Implementation
- [ ] Use privacy-preserving techniques
- [ ] Implement security controls
- [ ] Set up monitoring and logging
# 4. Documentation
- [ ] Update processing activities record
- [ ] Document technical measures
- [ ] Create user-facing privacy notices- Before accessing data: Verify you have proper authorization and purpose
- During analysis: Use the least sensitive data possible for the task
- After completion: Securely delete temporary files and datasets
- When in doubt: Consult with your Data Protection Officer (DPO)
| Context | Red Flag | Action Required |
|---|---|---|
| Data Collection | "Collect everything, we might need it later" | Push back - insist on data minimization |
| Model Development | "Don't worry about bias, focus on accuracy" | Conduct fairness assessment anyway |
| Data Retention | "Keep data forever, storage is cheap" | Implement automated retention policies |
| Third-Party Tools | "Just use it, we'll check compliance later" | Conduct vendor assessment before integration |
| User Rights | "We'll handle those requests manually" | Automate data subject request handling |
| GDPR Article | Technical Focus | Key Requirements |
|---|---|---|
| Article 5 | Principles | Data minimization, purpose limitation, storage limitation |
| Article 6 | Lawful bases | Documented lawful basis for all processing |
| Article 15 | Right of access | Export capabilities for user data |
| Article 17 | Right to erasure | Complete deletion from all systems |
| Article 20 | Data portability | Structured, machine-readable exports |
| Article 22 | Automated decisions | Human review, explanation, opt-out |
| Article 25 | Privacy by design | Default privacy settings, PETs |
| Article 30 | Records of processing | Documentation of data flows |
| Article 32 | Security | Appropriate technical measures |
| Article 35 | DPIA | Risk assessment for high-risk processing |
- Great Expectations - Data validation
- Presidio - PII detection and anonymization
- TensorFlow Privacy - Privacy-preserving ML
- PySyft - Federated learning framework
Internal Contacts:
- Data Protection Officer
- Legal Team
- Security Team
External Resources:
Remember: GDPR compliance is not a one-time project but an ongoing commitment. Your technical decisions shape our privacy posture. When in doubt, ask: "Is this the most privacy-preserving way to achieve our goal?"
\
- A. Comparison with Other Regulations
- B. Sample Code Repository
- C. Interactive Learning Modules
- D. Version History
🎯 Start Here: New to GDPR? Begin with GDPR Fundamentals
🔧 Ready to Implement? Jump to Privacy by Design
🤖 Working with AI/ML? Check ML Specifics
🔧 Ready to Implement? Jump to Privacy by Design
🤖 Working with AI/ML? Check ML Specifics