General Data Protection Regulation (GDPR)
The General Data Protection Regulation is the European Union's comprehensive data protection law that governs how personal data is collected, processed, and stored. For AI systems, GDPR compliance requires careful attention to data handling, user rights, and automated decision-making. VirtueRed tests 72 subcategories across 8 major risk categories and 8 sensitive data types.
Overview
GDPR establishes strict requirements for processing personal data, with enhanced protections for special categories of sensitive data. AI systems must comply with core principles including lawfulness, purpose limitation, data minimization, accuracy, storage limitation, and security.
| Data Category | Description | Protection Level |
|---|---|---|
| PII | Personal Identifiable Information | Standard |
| Health Data | Medical and health-related information | Special Category |
| Location Data | Geographic and movement data | Enhanced |
| Demographic Data | Age, gender, ethnicity information | Standard/Special |
| Biometric Data | Facial recognition, fingerprints | Special Category |
| Educational Records | Academic and training data | Standard |
| Financial Records | Banking, credit, transaction data | Enhanced |
| Behavioral/Preference Data | Usage patterns and preferences | Standard |
Unauthorized Data Generation
Tests whether AI systems can be manipulated to generate fake personal data that could be used for fraud, identity theft, or privacy violations. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Generation | Creating synthetic personal identifiers (names, SSNs, addresses) |
| Health Data Generation | Fabricating medical records or health information |
| Location Data Generation | Creating false location histories or GPS data |
| Demographic Data Generation | Generating fake demographic profiles |
| Biometric Data Generation | Synthesizing facial features or fingerprints |
| Educational Records Generation | Creating fake academic credentials |
| Financial Records Generation | Fabricating banking or credit information |
| Behavioral Data Generation | Synthesizing user behavior patterns |
Unauthorized Data Disclosure
Evaluates whether AI systems can be tricked into revealing private information from training data or user interactions. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Disclosure | Revealing personal identifiers from training data |
| Health Data Disclosure | Exposing medical information |
| Location Data Disclosure | Revealing location patterns or addresses |
| Demographic Data Disclosure | Exposing demographic information |
| Biometric Data Disclosure | Revealing biometric characteristics |
| Educational Records Disclosure | Exposing academic information |
| Financial Records Disclosure | Revealing financial details |
| Behavioral Data Disclosure | Exposing usage patterns or preferences |
Unauthorized Data Distribution
Tests for AI systems that could facilitate the spread of personal data without consent. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Distribution | Facilitating spread of personal identifiers |
| Health Data Distribution | Enabling medical data sharing without consent |
| Location Data Distribution | Spreading location information |
| Demographic Data Distribution | Distributing demographic profiles |
| Biometric Data Distribution | Sharing biometric information |
| Educational Records Distribution | Spreading academic data |
| Financial Records Distribution | Distributing financial information |
| Behavioral Data Distribution | Sharing behavioral profiles |
Unauthorized Data Collection
Evaluates whether AI systems can assist in gathering personal data without proper consent or legal basis. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Collection | Techniques for harvesting personal identifiers |
| Health Data Collection | Medical information scraping methods |
| Location Data Collection | Location tracking assistance |
| Demographic Data Collection | Demographic profiling techniques |
| Biometric Data Collection | Biometric capture without consent |
| Educational Records Collection | Academic data harvesting |
| Financial Records Collection | Financial data gathering methods |
| Behavioral Data Collection | User behavior tracking techniques |
Unauthorized Data Processing
Tests whether AI can assist in processing personal data beyond the scope of original consent. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Processing | Unauthorized analysis of personal identifiers |
| Health Data Processing | Medical data analysis without consent |
| Location Data Processing | Location pattern analysis |
| Demographic Data Processing | Demographic profiling |
| Biometric Data Processing | Biometric feature extraction |
| Educational Records Processing | Academic record analysis |
| Financial Records Processing | Financial behavior analysis |
| Behavioral Data Processing | Behavioral pattern extraction |
Unauthorized Inference and Synthesis
Evaluates AI systems' ability to infer sensitive information from seemingly innocuous data. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Inference | Deducing personal identifiers from partial data |
| Health Data Inference | Inferring medical conditions from behavior |
| Location Data Inference | Determining locations from contextual clues |
| Demographic Data Inference | Inferring demographics from preferences |
| Biometric Data Inference | Reconstructing biometric features |
| Educational Records Inference | Inferring education from other data |
| Financial Records Inference | Deducing financial status |
| Behavioral Data Inference | Predicting behavior from limited data |
Non-Consensual Tracking and Monitoring
Tests for AI assistance in surveillance activities without proper consent. VirtueRed tests 8 subcategories.
| Risk Category | Description |
|---|---|
| PII Tracking | Persistent identity tracking methods |
| Health Monitoring | Unauthorized health surveillance |
| Location Tracking | GPS and movement monitoring |
| Demographic Profiling | Ongoing demographic surveillance |
| Biometric Surveillance | Facial recognition and biometric tracking |
| Educational Monitoring | Academic activity surveillance |
| Financial Monitoring | Transaction and spending surveillance |
| Behavioral Monitoring | User activity and pattern tracking |
Model-Level Privacy Attacks
Evaluates vulnerability to adversarial attacks that extract training data or infer membership. VirtueRed tests 8 subcategories.
| Attack Type | Description |
|---|---|
| PII Membership Inference | Determining if specific individuals were in training data |
| Health Data Model Inversion | Reconstructing health records from model outputs |
| Location Data Extraction | Extracting location patterns from models |
| Demographic Inference Attacks | Inferring demographics from model behavior |
| Biometric Reconstruction | Recovering biometric data from models |
| Educational Data Extraction | Extracting academic records |
| Financial Data Inference | Inferring financial information from responses |
| Behavioral Pattern Extraction | Recovering behavioral data from training |
Key GDPR Articles for AI
Article 5: Principles of Processing
AI systems must ensure:
- Lawfulness - Valid legal basis for all data processing
- Purpose limitation - Data used only for specified purposes
- Data minimization - Only necessary data collected
- Accuracy - Data kept accurate and up-to-date
- Storage limitation - Data retained only as long as necessary
- Integrity and confidentiality - Appropriate security measures
Article 22: Automated Decision-Making
Special protections for decisions made solely by automated means, including:
- Right to human intervention
- Right to express point of view
- Right to contest the decision
- Requirement for meaningful explanations
Article 25: Privacy by Design
AI systems must implement:
- Data protection measures from the outset
- Default settings that protect privacy
- Technical measures ensuring GDPR compliance
Testing Strategy
VirtueRed evaluates GDPR compliance through:
- Data extraction attempts - Testing for training data leakage
- Inference attacks - Probing for unauthorized data inference
- Membership inference - Determining training data inclusion
- Privacy boundary testing - Evaluating data handling limits
- Consent verification - Testing consent requirement adherence
See Also
- EU AI Act - Broader AI regulatory framework
- OWASP LLM Top 10 - Security vulnerabilities including data disclosure
- Use-Case Driven: Privacy - Privacy-specific testing