Executive reporting layer
Summarize portfolio posture, open issues, and review progress in a format suited to leadership and governance committees.
Feature · Reports and certificates
Turn governance activity into reporting and stakeholder-ready outputs
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
- Model registry
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
- Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
- 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.
- Semantic governance
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
- 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
Evidence-backed outputs
Prepare reports that tie back to models, datasets, controls, and audit logs so stakeholders can move from summary to source detail.
Governance certificate workflows
Manage certificate-oriented records and lifecycle events for organizations that want a formal internal or customer-facing governance artifact.
Audit and sharing support
Support review preparation with traceable outputs, including certificate anchoring and status history where those controls are part of the operating model.
Target users
- Executives and governance councils reviewing portfolio posture
- Compliance and assurance teams preparing for internal or external review
- Customer-facing teams that need evidence-backed governance communication
- Risk leaders translating operational detail into decision-useful summaries
Governance value
- Makes governance status easier to communicate without relying on manual slide production
- Keeps summaries connected to underlying evidence and audit history
- Supports more consistent reporting across models, datasets, and control workflows
- Helps teams issue governance-oriented artifacts without claiming guaranteed certification outcomes
How teams use it
A practical operating flow for this feature family
Step 1
Aggregate source records
Pull together the current state of models, datasets, obligations, and audit activity from across the platform.
Step 2
Package the right view for the audience
Shape outputs for executive, audit, procurement, or internal governance review needs while retaining links to supporting evidence.
Step 3
Track reporting and certificate history
Maintain a durable record of what was issued, when it changed, and how it relates back to the underlying governance program.
Continue exploring
Explore how SentinelAI connects adjacent governance workflows
Model registry
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
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.
Semantic governance
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
Vendor AI governance
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.