The Audit tells finance leaders what is happening. Governance turns material findings into controls, evidence, and review cadence.
AI Audit and AI Governance are connected but distinct. The AI Audit produces the operating read across AI value, AI risk, Shadow AI, usage, spend, and evidence gaps. AI Governance turns material findings into policies, controls, owners, baselines, and framework-mapped evidence.
The Audit comes before the governance plan.
Governance work lands better when it is based on what is already running. Otherwise teams write policies for an AI estate they cannot see.
Audit: what AI is running, where it creates value, where it creates exposure.
Governance: what controls, policies, baselines, and evidence are needed.
Cadence: what needs quarterly, event-driven, or continuous monitoring.
Not every finding becomes governance work.
Some findings call for transformation, fluency, or spend consolidation. Governance is the right follow-on when the finding is material risk, policy exposure, eval coverage, or audit evidence.
Shadow AI in regulated workflows becomes governance work.
Unused licenses become spend or transformation work.
Low role-level capability becomes fluency work.
Governance needs current evidence.
AI systems change through model upgrades, prompt edits, tool access, and user behavior. Governance needs evidence that reflects the current state, not a point-in-time screenshot.
Policy coverage tied to production behavior.
Baseline and threshold history for high-risk use cases.
Incident traces, stale-evidence flags, and remediation owners.
AI Audit questions, answered plainly.
Questions buyers actually ask.
Start with the AI Audit when the organization cannot clearly answer what AI is running, what it is doing, and where the material risks sit.
Governance becomes the next workstream when the audit finds material policy gaps, evidence gaps, eval gaps, or regulatory exposure.
It can, but it often becomes policy-first work with weak operating signal. The Audit gives governance teams a real estate to govern.
Keep the audit path moving.
AI Governance
See the TrustEvals governance workstream.
AI Audit
See the diagnostic that precedes the governance plan.
Continuous evaluation
Read why AI evidence needs to stay current.
Baseline problem
Set thresholds before governance turns into opinion.
AI governance self-assessment
Score the control layers that governance has to close.
Turn the AI read into a decision.
Bring one workflow, vendor, or AI portfolio. We will map the evidence needed for finance leaders to fund, ship, or stop it.