Data Governance
Training data quality, provenance tracking, and data protection for AI
Overview
Data Governance for AI systems ensures the quality, integrity, and appropriate use of data throughout the AI lifecycle. This includes training data management, data quality assurance, provenance tracking, and compliance with data protection regulations.
The EU AI Act requires that training, validation, and testing data sets are subject to appropriate data governance and management practices, including examination for possible biases and data quality verification.
Key Elements
- Training data quality standards
- Data provenance and lineage tracking
- Bias detection in datasets
- Data protection compliance (GDPR, etc.)
- Synthetic data governance
- Data retention and deletion policies
Regulatory Requirements
Specific regulatory provisions addressing data governance.
EU AI Act
The EU AI Act requires comprehensive data governance measures for high-risk AI systems.
View full regulation →Colorado AI Act
Colorado's comprehensive AI Act includes specific requirements related to data governance.
View full regulation →ISO/IEC 42001
The international AI management system standard provides a framework for data governance.
View full standard →Why This Matters
Training data liability is huge. Companies have faced significant penalties for failures in this area. The EU AI Act provides for fines up to 35 million EUR or 7% of global turnover for serious violations.
Quick Actions
Premium tools for building policies and generating compliance checklists are in development.
Related Areas
- 4
Human Oversight & Ethical Safeguards
Human-in-the-loop requirements and ethical guardrails for AI systems
- 5
Transparency & Disclosure Requirements
AI system disclosure obligations and user notification requirements
- 7
Testing & Validation
Pre-deployment testing, conformity assessment, and ongoing monitoring
Need Help?
Our AI assistant can help you understand governance requirements and how they apply to your organization.