Feature · Model registry
SentinelAI gives governance teams a shared workspace for model registration, use-case alignment, ownership, versioning, lifecycle state, and decision history so intake and review work do not depend on spreadsheets or disconnected tickets.
What this area covers
The model registry is designed for teams that need a reliable inventory of AI systems across intake, validation, deployment, and ongoing oversight. It brings business context, linked use-case details, training-data references, risk posture, and governance records into a single operating view.
Related product areas
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.
Core capabilities
Register models with ownership, deployment status, intended use, prohibited-use status, linked use-case context, and training-data references so reviews start from consistent metadata.
Track how each model moves from intake through approval and operational use, with the surrounding governance context attached to the same record.
Keep the business purpose, application context, and operating expectations tied to the governed model record instead of buried in separate intake forms.
Keep related evidence, compliance status, monitoring summaries, and audit activity close to the model record instead of scattered across systems.
Give compliance, risk, and technical teams a shared inventory that supports prioritization, follow-up reviews, and cross-model comparison.
Target users
Governance value
How teams use it
Step 1
Capture core model attributes, ownership, deployment intent, and linked use-case context as the foundation for later review work.
Step 2
Connect documentation, dataset references, obligations, and audit activity to the same model workspace.
Step 3
Use the registry as the persistent record teams return to when the model changes, is reviewed, or moves to a new lifecycle state.
Continue exploring
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.