Feature · Reports and certificates
SentinelAI helps teams move from raw governance data to structured reporting for executives, auditors, customers, and internal review bodies, while keeping the underlying evidence trail connected to the source records.
What this area covers
Reporting and certificate workflows are meant for organizations that need to communicate governance status clearly. SentinelAI supports executive dashboards, evidence-backed reporting, and governance certificate records, but it does not imply automatic regulatory approval or third-party certification.
Related product areas
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
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.
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.
Core capabilities
Summarize portfolio posture, open issues, and review progress in a format suited to leadership and governance committees.
Prepare reports that tie back to models, datasets, controls, and audit logs so stakeholders can move from summary to source detail.
Manage certificate-oriented records and lifecycle events for organizations that want a formal internal or customer-facing governance artifact.
Support review preparation with traceable outputs, including certificate anchoring and status history where those controls are part of the operating model.
Target users
Governance value
How teams use it
Step 1
Pull together the current state of models, datasets, obligations, and audit activity from across the platform.
Step 2
Shape outputs for executive, audit, procurement, or internal governance review needs while retaining links to supporting evidence.
Step 3
Maintain a durable record of what was issued, when it changed, and how it relates back to the underlying governance program.
Continue exploring
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
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.
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.