Feature · Dataset governance
SentinelAI extends governance beyond model cards by giving teams a dedicated dataset registry with lineage, approval state, taxonomy-backed classification, sensitivity controls, and enterprise catalog integration hooks.
What this area covers
Dataset governance supports teams that need to understand where training, validation, test, and inference data comes from, how it is classified, how it is approved, and whether it is appropriate for production use. The result is stronger traceability across the model lifecycle.
Related product areas
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Track governed runtime systems that combine models, approved use cases, datasets, release state, and readiness into one operational record.
Govern versioned prompts, retrieval settings, linked AI systems, and evaluation posture from a dedicated prompt operations record.
Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
Core capabilities
Track dataset purpose, type, quality, sensitivity level, and stewardship details in a domain built specifically for AI governance.
Use centrally managed taxonomy terms so teams can classify datasets, governance states, and rollout concepts with more consistency across the platform.
Represent upstream sources, downstream usage, and model-to-dataset relationships to support provenance reviews and impact analysis.
Move datasets through draft, review, approved, and deprecated states with a stored event trail instead of informal sign-off.
Apply quality gates before linking datasets to higher-stakes model states and connect to enterprise catalogs such as Collibra, Purview, Databricks, Alation, or custom systems.
Target users
Governance value
How teams use it
Step 1
Capture dataset type, purpose, stewardship, taxonomy-backed classifications, and sensitivity as soon as the asset enters a governance process.
Step 2
Record how datasets relate to one another, which models they support, and whether they have cleared the required review steps.
Step 3
Feed dataset readiness and quality signals into the broader decision-making process around model lifecycle changes.
Continue exploring
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Track governed runtime systems that combine models, approved use cases, datasets, release state, and readiness into one operational record.
Govern versioned prompts, retrieval settings, linked AI systems, and evaluation posture from a dedicated prompt operations record.
Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.