NIST's AI Risk Management Framework (AI RMF 1.0) is voluntary — but it has become the lingua franca for U.S. federal procurement, sector guidance, and enterprise board questions. Its four core functions — Govern, Map, Measure, Manage — map cleanly to operational governance if you stop treating them as documentation exercises.
Govern
Establish roles, policies, and accountability. In practice: define who approves AI deploys, what evidence is required, and how violations escalate. A dual-portal model — engineering workspace plus GRC console — makes Govern tangible instead of a policy PDF no one opens.
Map
Contextualize risks for each AI use case: data flows, affected stakeholders, failure modes, and dependencies. Governance intake forms should capture intended purpose, tools, models, and explicit non-goals. Regal AI automates initial mapping from intake to risk tier and recommended controls.
Measure
Quantify and track risk with tests and metrics. Assign compliance tests after conditional approval: PII redaction validation, prompt injection suites, tool allowlist checks. Store results as auditable artifacts, not email attachments.
Manage
Prioritize and act on measured risk — including runtime response. Deploy gates block ungoverned releases; runtime enforcers deny undeclared tool calls; policy alerts notify GRC when agents step outside approved manifests.
From framework to workflow
The gap most organizations hit is between Map/Measure (assessment) and Manage (enforcement). openRegal closes that gap with a single workflow: intake triggers assessment, evidence unlocks authorization, authorization binds policy to deploy, policy enforces at runtime. NIST functions become stages engineers and auditors can point to — not overlapping committee meetings.