Challenges This Addresses
- Turning Article 10 requirements into operational controls — Move beyond policies and documentation to repeatable governance practices that work across the AI data lifecycle.
- Establishing trust and fitness of AI data — Address data origin, quality, suitability, representativeness, bias, gaps, completeness, and intended context of use for training, validation, and testing datasets.
- Demonstrating governance with traceable evidence — Maintain lineage, approvals, quality assessments, policy actions, transformation histories, and audit records that help demonstrate how AI data was governed.
What You’ll Learn
- What Article 10 actually requires organizations to govern across training, validation, and testing datasets used by relevant high-risk AI systems.
- How to apply the seven-control model—Discover, Understand, Qualify, Protect, Enforce, Trace, and Prove to translate regulatory expectations into enterprise data governance practices.
- How to assess whether AI data is fit for purpose, including quality, suitability, representativeness, bias, gaps, completeness, and the intended operating environment.
- How to move from governance policies to active controls using classification, access controls, protection, policy enforcement, lineage, and human oversight.
- How to build an evidence trail for AI governance through dataset inventories, definitions, approvals, quality and bias assessments, lineage, enforcement records, and audit evidence.
Why This Matters for Chief Data Officers (CDOs)
Chief Data Officers are increasingly responsible for ensuring that enterprise data is not only accessible and high quality, but also trusted, governed, and fit for AI use. The EU AI Act raises the stakes by placing greater emphasis on the origin, quality, suitability, representativeness, and governance of data used in relevant high-risk AI systems. This white paper gives CDOs a practical framework to move from policy to execution—helping their teams discover, understand, qualify, protect, enforce, trace, and prove how AI data is governed. It provides a structured approach for strengthening AI readiness while creating the traceability and evidence needed to support regulatory compliance and trustworthy enterprise AI.
