Docs · Models
Model governance workflows
See how SentinelAI organizes governed model records, lifecycle context, and evidence around each AI system.
Overview
The model governance area helps teams maintain a current inventory of AI systems with ownership, intended use, lifecycle state, and supporting governance context. It is designed to reduce the manual effort required to assemble this picture repeatedly.
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.
Model records
Each model record is meant to capture the attributes and context teams need before they can review, approve, or monitor an AI system.
- Ownership, usage context, linked use-case context, and lifecycle state can live alongside the governed system record.
- Supporting evidence and related workflows can stay attached to the same record over time.
Review readiness
Model-level governance works best when reviewers can assess the system record together with use-case, dataset, compliance, semantic relationship, and monitoring signals instead of gathering them ad hoc.
Operating guidance
Treat the model registry as the source of truth for governed AI systems, then connect related workflows rather than duplicating lifecycle facts across multiple places.