Docs · Datasets
Dataset governance workflows
Learn how SentinelAI supports dataset inventory, lineage, approvals, sensitivity handling, and model-to-data traceability.
Overview
Dataset governance extends the operating model beyond AI systems alone. It helps teams document training, validation, test, inference, and other dataset assets with stewardship, quality, approval, and lineage context.
This page is part of the public SentinelAI documentation layer. It is meant to accelerate orientation and evaluation while staying aligned with the product’s governance-focused positioning and messaging guardrails.
Registry and classification
Datasets can be recorded with purpose, type, taxonomy-backed classifications, sensitivity, quality, and stewardship details that are useful during review.
Lineage and approvals
Lineage and approval workflows help teams understand how datasets relate to one another and whether they are appropriate for governed use.
- Track upstream/downstream relationships and model usage.
- Use shared taxonomy and relationship logic to keep dataset classification more consistent.
- Preserve approval state and the surrounding event history.
Catalog and model linking
Where enterprise catalogs, semantic governance, and model governance intersect, SentinelAI can help teams preserve a clearer trace from data assets to AI systems and decision points.