Model registry
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
- • Structured model records and intake depth
- • Lifecycle visibility
AI governance platform
See how the SentinelAI AI governance platform works end to end
SentinelAI connects model registration, runtime AI systems, datasets, prompt and RAG governance, evaluation suites, release records, governance cases, telemetry connectors, semantic operations, vendor review, compliance workflows, monitoring observations, and reporting so AI oversight can run as one platform instead of a set of disconnected reviews.
Platform overview
SentinelAI is designed to operationalize AI governance across the full lifecycle.
Use the overview branches for context, then move into the visible product proof and deeper workflow detail below. This page is the clearest product overview for buyers comparing AI governance platforms.
Platform overviewSentinelAI is built for organizations that need more than a point-in-time model inventory.
Platform overview
SentinelAI is built for organizations that need more than a point-in-time model inventory.
- Links intake records, evidence, classifications, approvals, observations, and reporting workflows.
- Keeps governance aligned to real product, data, risk, and compliance work.
Who typically uses itA cross-functional operating layer for shared governance execution.
Who typically uses it
A cross-functional operating layer for shared governance execution.
- AI governance and compliance leaders
- Risk, legal, audit, and procurement stakeholders
- Data science, ML, and product owners
- Executives reviewing portfolio posture and readiness
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Governed capabilities across the AI lifecycle
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Model, data, and cloud integrations
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Frameworks: EU AI Act, NIST, ISO 42001, ISO 27001, SOC 2, GDPR
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Tamper-evident, hash-chained audit evidence
End-to-end operating model
One workflow foundation from AI system intake to stakeholder-ready reporting.
These stages reflect how SentinelAI connects the runtime, prompt, retrieval, release, telemetry, and reporting product areas described across the platform today.
Register the operating contextStage 1Start with a governed inventory of use cases, models, AI systems, prompts, retrieval sources, datasets, vendors, owners, and intended use so later reviews are grounded in shared records.
Register the operating contextStage 1
Start with a governed inventory of use cases, models, AI systems, prompts, retrieval sources, datasets, vendors, owners, and intended use so later reviews are grounded in shared records.
- Use-case, model, AI-system, and dataset registration
- Prompt and RAG-source inventory
- Ownership and stewardship assignment
- Initial risk and business context
Standardize governance language and relationshipsStage 2Use taxonomy and ontology administration to keep classifications, relationship rules, and graph traversal consistent across governance, risk, security, legal, and ML teams.
Standardize governance language and relationshipsStage 2
Use taxonomy and ontology administration to keep classifications, relationship rules, and graph traversal consistent across governance, risk, security, legal, and ML teams.
- Taxonomy CRUD and ontology administration
- Relationship rules and impact analysis
- Graph-backed exploration with saved query entry points
Run structured review workflowsStage 3Route obligations, evaluations, release approvals, cases, evidence requests, and remediation actions to the right stakeholders across governance, risk, security, legal, and ML teams.
Run structured review workflowsStage 3
Route obligations, evaluations, release approvals, cases, evidence requests, and remediation actions to the right stakeholders across governance, risk, security, legal, and ML teams.
- Framework and control mapping
- Evaluation and release decisions
- Approval and exception handling
- Evidence collection with traceability
Keep oversight current after deploymentStage 4Connect telemetry observations, vendor refreshes, dataset changes, prompt updates, and operating changes back to the same governance records.
Keep oversight current after deploymentStage 4
Connect telemetry observations, vendor refreshes, dataset changes, prompt updates, and operating changes back to the same governance records.
- Telemetry connectors and live signal ingest
- Drift, fairness, and evaluation observations
- Dataset and vendor change review
- Follow-up cases over time
Report status and issue governance outputsStage 5Summarize posture for executives, auditors, customers, and governance councils without losing the path back to source evidence.
Report status and issue governance outputsStage 5
Summarize posture for executives, auditors, customers, and governance councils without losing the path back to source evidence.
- Portfolio-level reporting
- Evidence packs and stakeholder views
- Governance certificate workflows
Where it sits
One governance layer across your entire AI stack.
Scroll to see how SentinelAI sits between the systems you already run and the auditors, regulators, and executives who need proof.
Your data & AI stack
Where models, data, and agents already live
Clouds & warehousesModel providersApps & autonomous agents
Ingestion & shadow-AI discovery
SentinelAI governance layer
The operating layer that sits across the stack
Inventory & intakeControls & approvalsRuntime output safetyTamper-evident audit chain
Governed, monitored, evidenced
Governed AI in production
Shipped with oversight, not blocked by it
Approved modelsSupervised agentsCustomer-facing AI
Evidence on demand
Auditors, regulators & the board
Proof they can independently verify
Regulator-ready packagesExternal audit portalExecutive reporting
The platform, end to end
Follow the governance narrative through the working product.
Each step of the operating model maps to a real workspace in SentinelAI — from inventory and data governance through use-case intake and evidence-backed framework review.
Inventory of record
Register every model and AI system in one governed inventory
Start from a single system of record for models, runtime AI systems, and the use cases behind them — with intended use, owners, lifecycle status, and release references kept together so every later review is grounded in shared context.
- Model and AI-system inventories with lifecycle and readiness
- Use-case and deployment context linked to each record
- Ownership, stewardship, and release references in one place
Data governance
Track datasets and retrieval sources with lineage and approvals
Govern datasets and RAG sources with quality signals, taxonomy-backed classification, lineage, and approval state — so the data behind every AI system stays explainable and audit-ready rather than scattered across spreadsheets.
- Dataset registry with sensitivity, quality, and ownership
- RAG source registry, versioning, and approval state
- Lineage and catalog integration hooks
Use-case intake
Anchor governance in the business reason each AI system exists
Capture the business objective, application context, accountable owners, and review triggers in structured intake — so downstream evaluation, controls, and reporting all trace back to why an AI system was built.
- Business context and intended outcomes
- Stakeholder routing and intake readiness
- Use-case-to-model alignment across the portfolio
Review and evidence
Run framework reviews against evidence, not status fields
Map obligations from the EU AI Act, NIST AI RMF, and ISO 42001 to concrete evidence and owners at the model level — with checklist status, evidence actions, and governance tabs in the same workspace the review team already lives in.
- Framework-aligned obligations with checklist status
- Evidence requests, actions, and remediation tracking
- Model-level review tabs across the full workflow
Platform objects
Governance records stay linked instead of living in separate systems.
SentinelAI brings together the core runtime, prompt, retrieval, workflow, and evidence objects governance teams need to understand AI use end to end.
AI systems and model recordsRegister models and runtime AI systems with intended use, owners, lifecycle status, linked use-case context, release references, and supporting governance history.
AI systems and model records
Register models and runtime AI systems with intended use, owners, lifecycle status, linked use-case context, release references, and supporting governance history.
- Model and AI-system inventories
- Use-case and deployment context
- Lifecycle, readiness, and release references
Use cases and intake workflowsCapture the business objective, application context, accountable owners, and review triggers that explain why an AI system exists before downstream governance work begins.
Use cases and intake workflows
Capture the business objective, application context, accountable owners, and review triggers that explain why an AI system exists before downstream governance work begins.
- Business context and intended outcomes
- Stakeholder routing and intake readiness
- Use-case-to-model alignment
Datasets and lineageTrack datasets and governed retrieval sources with quality signals, taxonomy-backed classification, lineage, ingestion posture, and approval state.
Datasets and lineage
Track datasets and governed retrieval sources with quality signals, taxonomy-backed classification, lineage, ingestion posture, and approval state.
- Dataset registry and lineage
- RAG source registry and versioning
- Approval and deprecation states
- Catalog integration hooks
Prompt and retrieval operationsGovern versioned prompts, retrieval configuration, linked RAG sources, and the AI systems they influence from dedicated operational records.
Prompt and retrieval operations
Govern versioned prompts, retrieval configuration, linked RAG sources, and the AI systems they influence from dedicated operational records.
- Prompt registry and prompt kinds
- Retrieval configuration and source linkage
- Prompt-level test cases and evaluation posture
Taxonomy, ontology, and graph operationsRun semantic governance in Operations with shared taxonomy terms, ontology entity and relationship administration, and graph-backed traversal across connected records.
Taxonomy, ontology, and graph operations
Run semantic governance in Operations with shared taxonomy terms, ontology entity and relationship administration, and graph-backed traversal across connected records.
- Taxonomy CRUD and controlled vocabularies
- Ontology type and relationship editor
- Dedicated graph explorer, saved queries, and impact summaries
Vendors and third-party AIKeep external providers, due-diligence artifacts, review notes, and procurement follow-up connected to the same governance program.
Vendors and third-party AI
Keep external providers, due-diligence artifacts, review notes, and procurement follow-up connected to the same governance program.
- Vendor posture and documentation
- Third-party review tasks
- Shared view for procurement and risk
Controls and framework mappingsMap internal policies and external frameworks such as the EU AI Act, NIST AI RMF, and ISO 42001 to concrete evidence and owners.
Controls and framework mappings
Map internal policies and external frameworks such as the EU AI Act, NIST AI RMF, and ISO 42001 to concrete evidence and owners.
- Framework-aligned obligations
- Evidence requests and status
- Remediation and exception tracking
Evaluation suites and release recordsTie prompt regression evidence, baselines, release approvals, rollback references, and dependency invalidation into one governed readiness loop.
Evaluation suites and release records
Tie prompt regression evidence, baselines, release approvals, rollback references, and dependency invalidation into one governed readiness loop.
- Baseline-aware evaluation suites
- Release-blocking thresholds
- Release records and rollback pointers
Governance cases and follow-upCoordinate alerts, findings, release exceptions, remediation tasks, evidence posture, and SLA deadlines in a shared case-management layer.
Governance cases and follow-up
Coordinate alerts, findings, release exceptions, remediation tasks, evidence posture, and SLA deadlines in a shared case-management layer.
- Case intake from findings and alerts
- Linked artifacts and assignment
- SLA tracking and closure outcomes
Observations and audit activityBring drift, fairness, evaluation, and operational observations into governance reviews while retaining an audit-ready trail of what changed.
Observations and audit activity
Bring drift, fairness, evaluation, and operational observations into governance reviews while retaining an audit-ready trail of what changed.
- Telemetry connectors and monitoring-linked oversight
- Decision and event history
- Cross-functional follow-up visibility
Reports and governance outputsTurn source records into executive reporting, stakeholder-ready evidence packs, and governance certificate workflows.
Reports and governance outputs
Turn source records into executive reporting, stakeholder-ready evidence packs, and governance certificate workflows.
- Executive reporting layer
- Evidence-backed summaries
- Certificate-oriented workflows
Impact radius
Linked records mean you can trace any change before it ships.
Because objects stay connected, you can follow a dataset through the models and decisions it feeds — and see the compliance risk each step inherits. Hover a node to trace downstream impact.
Data lineage
Hover a node to trace impactIllustrative. In product, lineage and impact-radius run across your real datasets, models, and controls.
Product in practice
See the operating layer in the product, not just in diagrams.
These product views show how SentinelAI turns the operating model into a working interface across portfolio oversight, model review, datasets, discovery, and related governance workflows.
Product proof
Portfolio overview dashboard
A portfolio-level command view with lifecycle metrics, risk heat mapping, compliance trend, and framework coverage.
Product proof
Model governance workspace
Model-level oversight with Article 9 checklist status, evidence actions, and governance tabs across the full review workflow.
Product proof
Dataset registry
Dataset inventory with sensitivity, approval status, quality signals, ownership, and governance-ready filtering.
Product proof
AI discovery and vendor oversight
Discovery and third-party AI views that help teams surface assets, track connectors, and govern external providers inside the same operating model.
Workflow design
Structure governance work around accountable teams and durable records.
Use these branches to understand why the workflow is structured this way and what teams gain from a shared operating layer.
Workflow designStructure governance work around accountable teams and durable records.
Workflow design
Structure governance work around accountable teams and durable records.
- Model builders, data owners, compliance teams, risk leaders, and executives can contribute to the same record.
- Framework reviews, evidence collection, monitoring follow-up, semantic analysis, and reporting remain connected to the governed assets.
Shared source of truthInstead of tracking AI systems, use cases, datasets, obligations, and reviews in separate tools, teams work from linked records that stay current over time.
Shared source of truth
Instead of tracking AI systems, use cases, datasets, obligations, and reviews in separate tools, teams work from linked records that stay current over time.
Cross-functional workflow clarityCompliance, risk, procurement, security, data science, and business owners can each see the work and decisions that belong to them.
Cross-functional workflow clarity
Compliance, risk, procurement, security, data science, and business owners can each see the work and decisions that belong to them.
Evidence that stays attached to decisionsApprovals, observations, remediation work, and reporting remain connected to the underlying systems they describe.
Evidence that stays attached to decisions
Approvals, observations, remediation work, and reporting remain connected to the underlying systems they describe.
Faster AI adoption with less governance driftStructured intake, shared taxonomy, and graph-backed relationship views reduce the friction that often slows AI application development and value realization.
Faster AI adoption with less governance drift
Structured intake, shared taxonomy, and graph-backed relationship views reduce the friction that often slows AI application development and value realization.
Explore the platform in more detail
Use these feature and docs paths to go deeper.
The platform page provides the full narrative; the links below route visitors into the specific product areas and supporting content behind it.
Feature deep dives
AI systems
Track governed runtime systems that combine models, approved use cases, datasets, release state, and readiness into one operational record.
- • Runtime system records
- • Linked governed dependencies
Prompt registry
Govern versioned prompts, retrieval settings, linked AI systems, and evaluation posture from a dedicated prompt operations record.
- • Versioned prompt records
- • Template and system-prompt visibility
RAG sources
Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.
- • Governed source registry
- • Ingestion and activation state
Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
- • Framework-aligned tracking
- • Evidence capture
Dataset governance
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
- • Dedicated dataset registry
- • Taxonomy-backed governance
Semantic governance
Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.
- • Operations-hosted semantic admin workspace
- • Full taxonomy administration
Evaluation suites
Define governed prompt evaluation suites with baselines, regression thresholds, run evidence, and release-blocking posture.
- • Suite definitions tied to AI systems
- • Prompt-linked test case inheritance
Release governance
Manage AI-system release records with approval state, rollback references, dependency snapshots, and invalidation handling.
- • Release records for AI systems
- • Approval and promotion workflow
Governance cases
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
- • Case intake for multiple trigger types
- • Linked artifact coordination
Telemetry connectors
Manage telemetry providers, ingest cadence, connector health, and manual signal pulls from a first-class governance control plane.
- • Provider-aware connector inventory
- • Health and status visibility
AI governance intelligence
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
- • Governance risk and control-gap detection
- • Duplicate and overlap detection
LLM telemetry and monitoring
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
- • Telemetry connector management
- • Live signal ingestion
Reports and certificates
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
- • Executive reporting layer
- • Evidence-backed outputs
Vendor AI governance
Register third-party AI vendors, structure due diligence, and connect external AI dependencies to internal governance records.
- • Governed vendor registry
- • Repeatable due-diligence workflows
Agentic AI governance
Govern autonomous and multi-agent AI with declared tool permissions, autonomy levels, memory scope, and full chain-of-custody across agent executions.
- • Agent registration with declared scope
- • Chain-of-custody tracing
Real-time output safety
Monitor live LLM outputs for toxicity, PII leakage, prompt injection, and hallucination against per-model thresholds, using privacy-preserving request hashes.
- • Live safety signals
- • Per-model thresholds
Shadow AI discovery
Find unsanctioned and ungoverned AI across the business through detection sources and CSV import, risk-score candidates, and register them into governed inventory.
- • Multi-source discovery
- • Risk scoring
AI carbon and energy governance
Estimate per-model CO2e and energy use from training compute, set monthly carbon budgets per tenant, and roll sustainability up to a portfolio view.
- • Per-model footprint estimates
- • Carbon budgets per tenant
Regulatory intelligence radar
Track changing AI regulation with auto-ingested updates matched to a tenant regulatory profile, impact assessment, and a radar timeline that routes changes to owners.
- • Auto-ingested regulatory updates
- • Profile-based matching
Vendor contract intelligence
Track vendor contracts and clauses, score clauses for missing data-protection and SLA terms, alert on 90-day expiries, and pair with a versioned due-diligence questionnaire builder.
- • Contract and clause tracking
- • Clause gap scoring
AI bill of materials
Generate a content-hashed AI Bill of Materials capturing model, dataset, and framework provenance, flag license conflicts, and export CycloneDX, SPDX, and PDF.
- • Content-hashed provenance
- • Model, dataset, and framework capture
AI compliance assistant
Answer compliance questions in natural language grounded in a regulatory knowledge base, with proactive nudges and session history across EU AI Act, NIST AI RMF, ISO 42001, and GDPR.
- • Grounded natural-language Q&A
- • Multi-framework coverage
Related docs and explainers
Related explainer
How SentinelAI works
Follow the operating model step by step, from AI system intake through review, monitoring, and executive reporting.
Workflow walkthroughRelated docs
Documentation hub
Review the reusable docs experience and supporting content patterns that will expand into deeper platform guides and references.
Docs entry point
Next step
Turn the platform overview into a live SentinelAI evaluation.
Use this page to align stakeholders on the operating model, then move into a demo, docs review, or product trial based on your team’s stage.