Operations-hosted semantic admin workspace
Run semantic governance from a dedicated admin surface instead of treating taxonomy and relationship logic as hidden configuration spread across the product.
Feature · Semantic governance
Run AI governance with shared taxonomy, ontology, and graph context
SentinelAI gives operators an Operations-hosted semantic governance workspace with taxonomy CRUD, ontology entity and relationship administration, and a graph console for explainable cross-object traversal.
What this area covers
Semantic governance is designed for teams that need more than isolated records. It helps operators define shared terms, relationship rules, and graph-backed navigation so models, use cases, datasets, controls, and governance documents stay connected as one operating system.
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.
- Prompt registry
Govern versioned prompts, retrieval settings, linked AI systems, and evaluation posture from a dedicated prompt operations record.
- RAG sources
Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.
- Dataset governance
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
- Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.
Core capabilities
Built to support production governance work
Full taxonomy administration
Create and manage controlled vocabularies, taxonomy records, hierarchical terms, aliases, ownership metadata, and activation state so governance classifications stay consistent across workflows.
Ontology type and relationship editor
Inspect and manage entity types plus relationship types with operational metadata, key preservation, source-target rules, and explainable change control.
Dedicated graph explorer and console
Traverse neighborhoods, impact summaries, framework mappings, and propagation views from one console instead of pivoting manually across multiple records.
Saved query entry points
Reuse graph-aware governance questions as operational shortcuts for cross-object review, follow-up investigation, and stakeholder explanation.
Target users
- Governance operators administering shared taxonomy and ontology rules
- Compliance and risk teams investigating cross-object impact and obligation context
- Data and ML leaders who need clearer links between use cases, models, and governed datasets
- Platform administrators expanding governance operations without switching to a separate graph product
Governance value
- Creates shared language for classifications and relationship semantics across the product
- Makes cross-object governance reviews more explainable and less manual
- Improves impact analysis when controls, datasets, models, and use cases influence one another
- Supports faster AI adoption by reducing governance coordination friction during application delivery
How teams use it
A practical operating flow for this feature family
Step 1
Define shared taxonomies
Create and maintain taxonomies, term hierarchies, aliases, and activation state so classification and workflow language stay aligned across operators and domains.
Step 2
Manage ontology rules
Use ontology entity and relationship administration to make connected governance objects more explicit, version-aware, and reusable.
Step 3
Investigate connected impact
Start from a governed object or saved query, traverse related records, and explain propagation, framework mapping, and neighborhood context from one operational console.
Step 4
Reuse graph findings operationally
Carry semantic graph insights back into compliance reviews, dataset approvals, model readiness decisions, and stakeholder explanation without rebuilding the relationship story manually.
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.
Prompt registry
Govern versioned prompts, retrieval settings, linked AI systems, and evaluation posture from a dedicated prompt operations record.
RAG sources
Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.
Dataset governance
Bring datasets, lineage, approvals, taxonomy-backed controls, catalog integrations, and quality gates into the AI governance workflow.
Compliance workflows
Operationalize evidence collection, control tracking, remediation, and framework mapping across AI systems.