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Case Study
Integrating GDPR Compliance into AI Projects
Integrating GDPR compliance for AI systems requires a dual focus on data governance and quality management, enabling organizations to handle personal data responsibly while meeting AI Act requirements, reducing compliance risks, and building stakeholder trust through transparent practices.
Challenge
AI model providers and deployers can be considered both data controllers and processors under GDPR. They must ensure compliance with their legal responsibilities when handling data for AI models. Training data may contain personal data, and AI model usage can generate personal data even if initially excluded.
GDPR challenges that are often found when creating, testing and running AI models are:
- Ensuring data integrity (AI Act, Art. 5): How to determine if personal data is processed and compliant?
- Managing data governance: Defining which data can be used while maintaining transparency
- Maintaining data quality (AI Act, Art. 10): Identifying and addressing data quality issues proactively
Solution
DTA helps businesses comply with GDPR and the AI Act through a structured approach focusing on:
- Data Governance
- Implementing scope limitation to control data usage
- Ensuring transparency to track data collection purposes
- Maintaining documentation for traceability
- Data Quality Management
- Analyzing and correcting data quality issues early
- Continuous monitoring to detect inaccuracies or biases
- Regular audits to ensure ongoing compliance
Impact
- Regulatory Compliance: Assured alignment with GDPR and AI Act requirements
- Risk Mitigation: Reduced legal and financial risks from improper data usage
- Enhanced Trust: Increased stakeholder confidence through transparency and accountability
- Future-Proof Solution: Scalable and adaptable processes for evolving regulations
This approach enables businesses to integrate GDPR compliance into AI projects, ensuring robust governance and ethical data usage.