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
- • Model-to-use-case alignment
Features
Explore the SentinelAI AI governance software feature set
Browse the product areas that help teams govern AI systems, prompts, RAG sources, evaluation suites, release decisions, governance cases, telemetry connectors, model registration, datasets, semantic relationships, compliance workflows, reporting, vendor review, and monitoring from one AI governance platform.
Feature hub overview
Start with the governance areas that buyers, operators, and review teams need to understand first.
This hub now aligns with the reusable website templates while keeping the feature narrative grounded in SentinelAI's core positioning: a governance operating system for AI inventory, runtime systems, prompt and retrieval operations, release and evaluation controls, evidence-backed workflows, semantic relationship operations, live telemetry monitoring, and stakeholder reporting.
Built for cross-functional teams
- AI governance and compliance leaders
- Risk, legal, audit, and assurance stakeholders
- Data science, ML, and product owners
- Executives reviewing portfolio posture and readiness
Feature detail pages
Explore the major SentinelAI feature pages available today
Each card routes to a dedicated feature page with a clearer description of the workflow, target users, governance value, and related product areas.
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
- • Readiness posture
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
- • Linked AI systems and RAG sources
RAG sources
Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.
- • Governed source registry
- • Ingestion and activation state
- • Chunk and citation visibility
Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
- • Framework-aligned tracking
- • Evidence capture
- • Remediation workflow
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
- • Lineage and traceability
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
- • Ontology type and relationship editor
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
- • Baseline and regression controls
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
- • Dependency snapshots
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
- • Assignment and SLA management
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
- • Ingest cadence control
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
- • Explainable findings
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
- • Signal history and processing state
Reports and certificates
Prepare executive reporting, audit-ready evidence views, and governance certificate workflows without overstating outcomes.
- • Executive reporting layer
- • Evidence-backed outputs
- • Governance certificate workflows
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
- • Framework-aligned oversight
Adjacent governance areas
See how the feature hub connects to the rest of the SentinelAI platform
These adjacent areas help buyers move from dedicated feature pages into framework positioning, trust content, and rollout guidance without duplicating product-detail copy.
Related capability
Framework mappings
Position AI governance work against the EU AI Act, NIST AI RMF, and ISO 42001 without turning the feature hub into a framework microsite.
- • EU AI Act orientation
- • NIST AI RMF alignment
- • ISO 42001 positioning
Related capability
Security and access control
Support enterprise oversight with role-based access control, MFA-aware workflows, and multi-tenant isolation.
- • Role-aware workflows and tenant scoping
- • Evaluation-ready auditability framing
- • Deployment and trust-readiness context
Related capability
Docs and rollout guidance
Use the docs layer and platform explainers to carry feature discovery into evaluation planning and rollout preparation.
- • Public product documentation
- • Workflow explainers and orientation
- • Guided evaluation paths
How the hub maps the operating model
Use the hub as an SEO-friendly starting point for deeper product exploration
The navigation below mirrors how SentinelAI connects records, workflows, monitoring signals, and reporting outputs, with descriptive internal links instead of generic onward journeys.
Model, AI-system, and data system of record
Start with governed records for models, runtime AI systems, business use cases, datasets, prompts, and RAG sources so later reviews have clear ownership, lifecycle, and provenance context.
- • Model registry
- • AI systems
- • Prompt registry
- • RAG sources
- • Use-case intake
- • Dataset governance
- • Lifecycle visibility
Evaluation and release guardrails
Move from governed inventories into evaluation baselines, release records, rollback references, and dependency-aware approvals that make runtime promotion reviewable.
- • Evaluation suites
- • Release governance
- • Prompt-linked test cases
- • Dependency invalidation
Case-driven investigations and remediation follow-up
Coordinate findings, alerts, release exceptions, remediation, evidence posture, and SLA ownership from a shared governance case layer instead of scattered tickets.
- • Governance cases
- • Compliance workflows
- • Linked artifacts
- • SLA tracking
Telemetry control plane and ongoing oversight
Manage telemetry connectors, ingest cadence, signal history, and monitoring-aware follow-up so governance can stay current after release.
- • Telemetry connectors
- • LLM telemetry monitoring
- • Trigger-driven review actions
- • Evidence-ready signals
Next step
Take the next step from feature discovery into a live walkthrough
Use this feature overview to orient stakeholders, then move into documentation, a demo, or a trial based on where your governance program is today.