Data Governance Under the AI Act: Beyond GDPR Requirements

If you're reading this, you've probably already wrestled with GDPR compliance. You understand data protection, privacy rights, and consent mechanisms. But here's what might surprise you: the EU AI Act's Article 10 introduces data governance requirements that go significantly beyond GDPR. It's not just about protecting data anymore – it's about ensuring your data creates trustworthy AI.

Let's explore this new frontier together, building on what you know while preparing for what's coming.

The Fundamental Shift in Perspective

GDPR asks: "Is this data processed lawfully, fairly, and transparently?"

The AI Act asks: "Will this data create AI that works correctly, fairly, and safely?"

This shift from protection to performance changes everything about how we approach data governance. You're not just safeguarding data; you're ensuring it builds reliable, unbiased, and robust AI systems.

Think of it this way: GDPR ensures you have permission to use the ingredients. The AI Act ensures those ingredients will make something safe to consume.

What Article 10 Actually Requires

Article 10 sets out detailed requirements for training, validation, and testing datasets. Let's break down what this means practically:

Data Quality Requirements

The Mandate: "Training, validation and testing data sets shall be relevant, representative, free from errors and complete."

The Reality Check: Perfect data doesn't exist. The Act acknowledges this with "as far as possible" language, but you need to document your quality measures.

Practical Implementation:

• Define quality metrics for your specific use case
• Implement automated quality checks in your pipeline
• Document known limitations and their potential impacts
• Establish regular quality review cycles

One fintech company we worked with created a "Data Quality Scorecard":

• Relevance: 92% (some historical data less relevant)
• Representativeness: 87% (working to improve demographic coverage)
• Error rate: 0.3% (automated detection and correction)
• Completeness: 94% (some optional fields missing)

They don't claim perfection, but they demonstrate diligence.

Statistical Properties Documentation

The Mandate: "Training, validation and testing data sets shall have the appropriate statistical properties."

Beyond GDPR: GDPR doesn't care about your data's statistical distribution. The AI Act does.

What This Means:

• Document data distributions across relevant dimensions
• Identify and document any skewness or imbalances
• Show how statistical properties align with intended use
• Demonstrate awareness of potential biases

Practical Approach:

Create statistical profiles including:

• Distribution analyses (normal, skewed, multimodal)
• Class balance for classification tasks
• Temporal patterns and seasonality
• Correlation analyses between features
• Outlier detection and handling

The Representativeness Challenge

The Mandate: Data must be "representative of the intended purpose and deployment context."

The Complexity: Representativeness isn't universal – it's contextual.

Real-World Example:

A hiring AI trained on data from tech companies in Silicon Valley isn't representative for manufacturing companies in Poland. Same algorithm, different context, different requirements.

How to Document Representativeness:

• Define your target population explicitly
• Compare training data demographics with target population
• Identify gaps and document mitigation strategies
• Consider temporal representativeness (is old data still valid?)
• Document geographical and cultural considerations

Design Choices and Assumptions

The Mandate: Document "design choices relating to the data" and "assumptions made."

What This Really Means: Every decision about data needs rationale.

Key Design Choices to Document:

• Why you included or excluded certain data sources
• How you determined sample sizes
• Rationale for train/validation/test splits
• Feature selection and engineering decisions
• Handling of missing data
• Approach to synthetic data (if used)

Example Documentation:

"We excluded data from before 2021 because regulatory changes fundamentally altered customer behavior patterns. Including older data would introduce patterns that no longer apply, potentially degrading model performance in current conditions."

The Bias Detection and Mitigation Framework

This is where the AI Act goes well beyond GDPR. You're not just protecting against discrimination; you're actively detecting and mitigating bias.

Examination for Biases

The Requirement: "Examination in view of possible biases likely to affect health and safety or lead to discrimination."

The Practical Challenge: How do you examine for biases you might not know exist?

Systematic Approach:

1. Demographic Analysis: Break down performance by protected characteristics
2. Intersectional Analysis: Look at combinations (age + gender + ethnicity)
3. Proxy Detection: Identify features that might proxy for protected characteristics
4. Outcome Analysis: Examine disparate impact even without intent
5. Edge Case Testing: Specifically test underrepresented groups

The Protected Characteristics Paradox

Here's where it gets tricky: To detect bias against protected characteristics, you need data about those characteristics. But collecting such data raises privacy concerns.

The AI Act's Solution: Article 10(5) explicitly allows processing special categories of personal data when:

• Strictly necessary for bias monitoring and correction
• Appropriate safeguards are in place
• Used only for ensuring bias detection and correction

Practical Implementation:

• Collect protected characteristic data separately from training data
• Use it only for testing and validation
• Implement strict access controls
• Document the necessity for each characteristic collected
• Delete or anonymize after bias testing

One healthcare AI company created a "bias testing dataset" completely separate from their training infrastructure, accessed only during scheduled bias audits.

Data Governance Measures in Practice

The Living Data Pipeline

Static datasets are dead datasets. Your governance needs to handle continuous data flows:

Data Lineage Tracking:

• Source → Collection → Processing → Training → Deployment
• Version control for datasets (not just models)
• Ability to trace any prediction back to its training data
• Documentation of all transformations

Quality Gates:

Implement automated checks at each stage:

Raw Data → [Quality Check] → Cleaned Data → [Bias Check] →

Training Data → [Statistical Check] → Model Training

Annotation and Labeling Governance

Beyond GDPR Requirement: The quality of your labels directly impacts AI safety and performance.

Comprehensive Annotation Framework:

• Clear annotation guidelines (50+ pages for complex tasks)
• Annotator training and certification
• Inter-annotator agreement metrics
• Regular calibration sessions
• Audit trails for all annotations
• Handling of ambiguous cases

Real Example: An autonomous vehicle company requires:

• Three independent annotations for safety-critical labels
• 95% agreement threshold
• Expert review for disagreements
• Monthly annotator recalibration
• Detailed documentation of edge cases

Data Refresh and Drift Management

Your data governance isn't one-and-done:

Continuous Monitoring Requirements:

• Data drift detection (input distribution changes)
• Concept drift detection (relationship changes)
• Performance degradation alerts
• Automated retraining triggers
• Documentation of all updates

Practical Implementation:

• Set up monitoring dashboards
• Define drift thresholds
• Establish review cycles
• Document decisions to retrain (or not)
• Maintain historical performance records

Integration with Existing GDPR Processes

You don't need to start from scratch. Here's how to build on your GDPR foundation:

Enhanced Purpose Limitation

GDPR: Data collected for specified, explicit, and legitimate purposes.

AI Act Addition: Purposes must align with AI system's intended use and risk profile.

Practical Integration:

• Expand purpose statements to include AI-specific uses
• Document how each data element contributes to AI functionality
• Establish clear boundaries for AI vs. non-AI use

Upgraded Data Minimization

GDPR: Adequate, relevant, and limited to what's necessary.

AI Act Addition: Sufficient for reliable, unbiased AI performance.

The Tension: AI often needs more data for accuracy, but minimization principles still apply.

Resolution Strategy:

• Document why each feature is necessary for AI performance
• Show testing of reduced feature sets
• Implement progressive collection (start minimal, add if needed)
• Use synthetic data where possible to reduce real data needs

Extended Retention Policies

GDPR: No longer than necessary for purposes.

AI Act Addition: Consider model refresh and monitoring needs.

Practical Approach:

• Separate retention for training vs. production data
• Keep test sets longer for ongoing bias monitoring
• Document retention rationale for AI-specific needs
• Implement automated deletion with audit trails

Common Pitfalls and How to Avoid Them

Pitfall 1: Treating All Data Equally

Problem: Applying the same governance to all data regardless of impact.

Solution: Risk-based approach focusing on data that most affects AI outcomes.

Pitfall 2: Documentation After the Fact

Problem: Trying to document data decisions retroactively.

Solution: Document as you go. Create templates for common decisions.

Pitfall 3: Ignoring Synthetic Data

Problem: Assuming synthetic data has no governance requirements.

Solution: Synthetic data needs the same quality and bias checks as real data.

Pitfall 4: Static Bias Testing

Problem: One-time bias testing at development.

Solution: Continuous bias monitoring in production.

Pitfall 5: Siloed Governance

Problem: AI data governance separate from general data governance.

Solution: Integrated framework with AI-specific additions.

Industry-Specific Considerations

Healthcare

• Patient representativeness across conditions
• Temporal validity of medical data
• Handling of rare diseases in training data
• Cross-institutional data quality variations

Financial Services

• Economic cycle representativeness
• Regulatory change impacts on historical data
• Geographic and demographic fairness
• Fraud pattern evolution

Human Resources

• Historical bias in hiring data
• Changing job market dynamics
• Cultural and linguistic considerations
• Skills taxonomy evolution

Retail

• Seasonal pattern handling
• Customer segment representation
• Price sensitivity variations
• Behavioral shift detection

Building Your Enhanced Data Governance Framework

Step 1: Gap Analysis (Month 1)

• Compare current GDPR governance with AI Act requirements
• Identify data-specific risks for your AI systems
• Map data flows and decision points
• Assess current quality and bias measures

Step 2: Framework Design (Month 2)

• Define quality metrics and thresholds
• Design bias detection processes
• Establish documentation templates
• Create governance workflows

Step 3: Implementation (Months 3-4)

• Deploy quality monitoring tools
• Implement bias detection systems
• Train teams on new processes
• Start documentation practices

Step 4: Validation (Month 5)

• Test governance processes
• Validate bias detection effectiveness
• Review documentation completeness
• Refine based on findings

Step 5: Operationalization (Month 6)

• Integrate with development workflows
• Automate where possible
• Establish review cycles
• Create continuous improvement process

Tools and Technologies

Data Quality Tools

• Great Expectations (Python-based validation)
• Apache Griffin (data quality service)
• Deequ (AWS data quality)
• Custom quality dashboards

Bias Detection Tools

• Fairlearn (Microsoft)
• AI Fairness 360 (IBM)
• What-If Tool (Google)
• Custom bias metrics

Data Governance Platforms

• Collibra (enterprise governance)
• Alation (data catalog)
• Apache Atlas (metadata management)
• Custom solutions

Documentation Systems

• Confluence (collaborative documentation)
• GitBook (technical documentation)
• Jupyter Notebooks (executable documentation)
• Custom wikis

The Path Forward

Data governance under the AI Act isn't just enhanced GDPR compliance – it's a fundamental shift in how we think about data quality, representativeness, and fairness. The organizations succeeding are those that see this as an opportunity to build better AI, not just compliant AI.

Start by understanding your current data landscape. Build on your GDPR foundation. Add AI-specific quality and bias measures. Document thoroughly. Monitor continuously. Improve iteratively.

Remember: Good data governance makes good AI. The AI Act just ensures you do what you should be doing anyway – building AI systems that work reliably and fairly for everyone.

Your Immediate Action Items

1. This Week: Assess your current data quality measures
2. Next Two Weeks: Design bias detection processes
3. Next Month: Implement quality gates in your pipeline
4. Next Quarter: Fully operationalize enhanced governance

The August 2026 deadline might seem distant, but data governance transformation takes time. Start now, build incrementally, and by the deadline, you'll have not just compliance but genuinely better AI.

The future of AI is built on trustworthy data. The AI Act just makes sure we don't forget that fundamental truth.