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.

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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.

Built to support production governance work

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.

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.

Impact radius

Trace any change before it ships

Hover a node to light up everything downstream and surface the compliance risk it inherits — from a PII dataset to a high-risk model to an Article 22 automated decision.

Data lineage

Hover a node to trace impact

Illustrative. In product, lineage and impact-radius run across your real datasets, models, and controls.

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.

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