Discover
Identify Shadow AI, AI systems, decision points, data flows, coverage gaps, and unmanaged governance exposure.
Governance lifecycle
Discover, Assess, Enforce, Remediate, Prove.
The AI governance lifecycle is the operating model for controlling AI risk from unmanaged use through audit evidence.
Assess
Use EVF to evaluate execution viability, governance readiness, AI failure modes, adversarial sufficiency, evidence quality, drift, gate integrity, legal exposure, and regulatory readiness.
Enforce
Apply organisation-configurable governance controls at runtime. Return Allow, Warn, Hold, Block, or Stop before an AI-enabled action proceeds.
Remediate
Use PRISM reports to convert risk findings into governance, auditor, regulatory, and executive remediation outputs.
Prove
Generate sealed audit evidence so the organisation can demonstrate what happened, why it happened, which controls applied, and who was accountable.
On-prem | Sovereign cloud | Partner cloud | Federated
Why the lifecycle matters.
AI risk does not start at model launch and it does not end with a dashboard. It starts when unmanaged systems appear, grows when failure modes are not assessed, becomes exposure when actions run without controls, and becomes expensive when evidence has to be reconstructed after the fact.
Shadow AI Discovery
Find unmanaged AI, inventory decision points, and bring exposed systems into governance onboarding.
EVF Assessment
Evaluate failure modes, governance readiness, evidence quality, drift, and legal exposure.
Runtime Governance
Apply configurable admission policies, overrides, escalation, and Allow, Warn, Hold, Block, or Stop verdicts.
PRISM Remediation
Convert findings into governance, audit, regulatory, and executive remediation outputs.
Witness Audit Evidence
Create sealed, hash-chained evidence with named accountability and non-self verification.