Data Provenance: Why AI Regulation Will Require It From December 2027

Data provenance is the documented evidence of a dataset’s origin, permissions, consent, transformations, versions, and use in AI systems, while data lineage describes the technical path data takes through processing pipelines. The piece argues that provenance will become a significant compliance requirement for high-risk AI under the EU AI Act from December 2027, with providers expected to document dataset sources, legal bases, collection methods, preprocessing, labeling, limitations, and links to particular model versions; related disclosure requirements are also emerging in U.S. laws. It emphasizes that generic descriptions and self-reported metadata may not satisfy auditors, who seek independently verifiable records such as licenses, chain-of-custody documentation, and computation logs. Provenance is particularly difficult when sensitive data belongs to hospitals, banks, government bodies, or other external partners that cannot legally or practically centralize records, prompting the use of federated and privacy-preserving approaches that retain data locally while recording participating datasets and computations. The discussion recommends prioritizing high-risk and externally sourced data, embedding provenance collection throughout the model lifecycle, and developing documentation before regulatory audits, while presenting Duality’s platform as a tool for supporting cross-organization collaboration without pooling raw data.

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