How AI Governance Platform Works
AI Governance Platform is a platform around your AI systems: it governs versioned assets and runtime evidence, evaluates behavioral changes, reasons over policy and dependencies, and produces durable decisions and assurance evidence—without taking ownership of your applications or deployment systems.
The Platform Flow
It is an AI Governance Platform, not an evaluation-and-policy point product. It connects the full lifecycle of AI change: governed assets, runtime observations, evaluation, replay, policy, decisions, assurance, and operations.
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01
Your AI systems
Agents, applications, workflows, providers, deployment, and live traffic remain in your stack.
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02
Assets + evidence
Versioned managed assets and runtime observations establish governed facts and traceability.
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03
Evaluate + replay
Experiments, evaluation, comparison, and replay turn behavioral change into durable evidence.
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04
Govern + decide
Policy and dependency context produce explainable, versioned governance decisions.
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Analyze change
Enterprise change-assurance capabilities assess the consequences of a proposed AI change.
06
Assure + explain
Audit, lineage, events, and—where enabled—operational findings make the outcome reviewable and actionable.
This diagram intentionally stays at six stages. It explains the operating model; detailed component architecture belongs in the Architecture guide.
Three Governed Loops
01
Evaluation loop
Prompt / Model / Dataset → Experiment → Evaluation → Comparison → Recommendation
Which configuration behaves better?
02
Governance loop
Runtime evidence → Ontology → Policy → Decision → Evidence / Lineage
Was this AI activity compliant, explainable, and governed?
03
Change-assurance loop · Enterprise
Proposed change → Behavior Contract → Impact analysis → Replay / Evaluation → Release readiness
Is this change safe enough to proceed?
Enterprise intelligence and operations can layer over these loops: events, graph relationships, findings, and operational state become grounded recommendations about what needs attention and why.
One brand; explicit edition boundaries
AI Governance Platform uses the same governance model across its platform products. Evaluation, governed assets, policy, decisions, audit, replay, and lineage are OSS capabilities. Behavior Contracts, impact analysis, release readiness, and intelligence are Enterprise extensions and remain clearly identified as such.Cross-Cutting Control Planes
The three loops share platform capabilities rather than reimplementing them independently. This keeps governance operations tenant-aware, observable, auditable, and consistently available through Studio, REST, MCP, and events.
Ownership Boundary
Your stack owns
Application behavior, prompt deployment, model serving, workflow execution, infrastructure, CI/CD, and traffic control.
AI Governance Platform owns
Governance state, asset versions, evidence, policy versions, decisions, assurance records, audit context, events, and lineage.
A decision is not a deployment
A downstream release system may consume a AI Governance Platform decision, but AI Governance Platform does not deploy a model or mutate your runtime configuration.Choose Your Next Step
- Run the 10-minute local tour to inspect seeded governed records.
- Follow the pre-release evaluation story from governed inputs through decision review.
- Read the Policy Engine guide to understand deterministic decisions.