AI Data Governance

It connects technical data management with the purpose and permissions of the application. A dataset catalog is useful, but governance also requires decisions and accountable owners.

Viktige takeaways

• Record purpose and permitted uses.
• Include derived assets in lifecycle controls.
• Assign owners and verify operational procedures.

Inventory the data and its uses. Record where each dataset came from, why it is needed, who may access it, and whether its permissions cover training, retrieval, evaluation, or publication. Those uses are not automatically interchangeable. Track derived assets as well as originals. Extracted text, embeddings, cached responses, labels, and model checkpoints can retain information or dependencies from source data. A deletion process that removes only the uploaded file may leave relevant copies behind. Define quality and change controls. Document required fields, units, label rules, and validation checks. Assign an owner to approve schema changes and investigate errors. Preserve lineage so a problematic source or transformation can be traced to affected outputs. Review retention and access periodically, especially when a service gains new integrations or a model is adapted for a different purpose. Make the operational procedure clear: who handles a correction, how quickly it propagates, and how completion is verified. Governance should be visible in the working system rather than existing only as a policy document.

1. Imagine a document uploaded to a knowledge base, copied into extracted text, split into passages, and embedded for search.
2. List each derived store and its responsible service before designing deletion.
3. After an authorized deletion, verify that the document is absent from retrieval and caches according to the documented retention policy.

This constructed workflow shows why governance must account for the full data lifecycle.