Structured model records and intake depth
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.
Feature · Model registry
Maintain a governed system of record for AI models and intake context
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
- Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
- Dataset governance
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
- AI governance intelligence
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
- LLM telemetry and monitoring
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
- Reports and certificates
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
- Vendor AI governance
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.
Core capabilities
Built to support production governance work
Lifecycle visibility
Track how each model moves from intake through approval and operational use, with the surrounding governance context attached to the same record.
Model-to-use-case alignment
Keep the business purpose, application context, and operating expectations tied to the governed model record instead of buried in separate intake forms.
Model-level detail workspaces
Keep related evidence, compliance status, monitoring summaries, and audit activity close to the model record instead of scattered across systems.
Portfolio alignment
Give compliance, risk, and technical teams a shared inventory that supports prioritization, follow-up reviews, and cross-model comparison.
Target users
- AI governance teams building a central inventory of governed systems
- Compliance officers reviewing high-risk or policy-sensitive use cases
- Risk managers who need lifecycle and ownership clarity across the portfolio
- ML and product teams contributing operational context during intake and review
Governance value
- Reduces manual reconciliation across spreadsheets, tickets, and ad hoc documentation
- Improves intake discipline by keeping model and use-case context together from the start
- Creates a clearer handoff between model owners and governance stakeholders
- Improves traceability when teams need to review model purpose, risk tier, or evidence history
- Supports downstream workflows such as compliance reviews, monitoring, and reporting
How teams use it
A practical operating flow for this feature family
Step 1
Register and classify
Capture core model attributes, ownership, deployment intent, and linked use-case context as the foundation for later review work.
Step 2
Attach governance context
Connect documentation, dataset references, obligations, and audit activity to the same model workspace.
Step 3
Track change over time
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
Explore how SentinelAI connects adjacent governance workflows
Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
Dataset governance
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
AI governance intelligence
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
LLM telemetry and monitoring
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Reports and certificates
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
Vendor AI governance
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.