AI assuranceAI Audit Evidence Integrity
AI audit evidence integrity is the ability to show that logs, model artifacts, data extracts, test results, approvals, and screenshots are authentic, complete, attributable, and unchanged.
AI assuranceAI Audit Finding Follow-Up
AI audit finding follow-up verifies that corrective actions address the root cause, operate as intended, and reduce the stated risk before a finding is closed.
AI auditAI Audit Hallucination Controls
AI audit hallucination controls prevent unsupported generated statements from being treated as evidence, criteria, citations, calculations, or conclusions.
AI change assuranceAI Change Evidence Package
An AI change evidence package assembles the artifacts needed to understand, approve, reproduce, and later audit a model, data, prompt, configuration, or integration change.
AI resilienceAI Deactivation and Contingency Controls
AI deactivation and contingency controls let an organization pause, limit, localize, roll back, or replace an AI capability when risk exceeds tolerance.
AI delivery controlsAI Deployment Approval Gates
AI deployment approval gates require defined evidence and accountable authorization before models or AI-enabled features advance into higher-risk environments.