When GRC asks "can we prove this AI was governed?" they are not asking for a risk workshop deck. Auditors want artifacts that chain together: what was approved, what was tested, what shipped, and what happened in production.
The evidence chain
- Governance intake — documented use case, tools, data scope, and non-goals
- Risk assessment — tier, controls, and assigned compliance tests
- Conditional approval — who approved, with what conditions, on what date
- Test results — PII redaction, prompt injection, allowlist validation, HITL workflows
- Audit authorization — GRC sign-off linking evidence to deploy permission
- Deploy record — environment, policy token, and policy version
- Runtime logs — allow/deny decisions and violations post-deploy
Break any link and the audit fails — even if the AI works fine in practice.
Common compliance tests
- PII redaction — verify sensitive patterns masked before model calls
- Prompt injection suite — adversarial prompts; zero critical bypasses
- Tool allowlist validation — agent cannot invoke undeclared tools
- Human-in-the-loop — outbound actions require reviewer approval
Tests should be assigned automatically from risk assessment — not reinvented per project in email threads.
Pre-audit validation
Regal AI can check evidence completeness before GRC audit review: missing tests, stale results, or mismatched policy versions surface early. Auditors respect organizations that catch gaps before the audit — not during it.
Make evidence a system state
Store artifacts in the governance platform, not scattered across Drive folders. When evidence is a workflow state — submitted, validated, approved — audits become queries, not archaeology.