Feature · Compliance workflows
SentinelAI helps organizations structure recurring governance work around obligations, evidence, remediation, and review status across the EU AI Act, NIST AI RMF, ISO 42001, and internal policies.
What this area covers
Compliance workflows are built for teams that need governance activity to be repeatable and reviewable. Instead of tracking obligations in documents alone, SentinelAI organizes evidence, framework mapping, and unresolved gaps in a workspace tied to the underlying AI systems.
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.
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
Define governed prompt evaluation suites with baselines, regression thresholds, run evidence, and release-blocking posture.
Manage AI-system release records with approval state, rollback references, dependency snapshots, and invalidation handling.
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
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
Map model and portfolio activity to relevant obligations and controls across major AI governance frameworks and internal review programs.
Centralize supporting artifacts, review notes, and status updates so teams can prepare for internal review, audit, or external stakeholder questions with less scrambling.
Turn unresolved gaps into follow-up actions instead of leaving them as passive findings in a spreadsheet or slide deck.
Maintain assessment-oriented workflows and evidence trails for teams that need deeper review around regulated or high-risk AI use cases.
Target users
Governance value
How teams use it
Step 1
Associate models and programs with the frameworks, requirements, and internal controls that matter to them.
Step 2
Attach supporting materials, review status, and remediation actions in the same workflow where teams assess readiness.
Step 3
Summarize where obligations are progressing, where exceptions remain, and what needs executive or governance review next.
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.
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
Define governed prompt evaluation suites with baselines, regression thresholds, run evidence, and release-blocking posture.
Manage AI-system release records with approval state, rollback references, dependency snapshots, and invalidation handling.
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
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.