Phase 7: AI Governance, Compliance & Risk · 50 min · NIST AI RMF · Python

NIST AI Risk Management Framework

Govern, Map, Measure, Manage — or explain to your enterprise customer's security team why you skipped one.

Hiring signal: AI Governance & Compliance Engineer roles ($140k-$200k) and enterprise security questionnaires now routinely ask for NIST AI RMF alignment. Engineers who can turn 'establish policies for third-party risk' into a checked-in RACI matrix and a CI gate are the ones who actually pass procurement review, not just cite the framework by name.

What you will learn

Explain the purpose of each NIST AI RMF function — Govern, Map, Measure, Manage — and what evidence satisfies it
Translate RMF language into concrete engineering artifacts: policies, risk registers, eval gates, incident runbooks
Identify the generative-AI-specific risk categories added by the NIST-AI-600-1 profile
Run a structured self-assessment against an AI system and produce a prioritized gap report

The Problem

A mid-size SaaS company closes a deal with a large enterprise customer. The customer's security team sends over a vendor questionnaire, and one section asks: "Describe your alignment with the NIST AI Risk Management Framework." The engineering team that built the product's generative AI features has never heard the phrase. They have a decent eval suite and a threat model from a security review six months ago — but no idea how to map that into an answer a procurement reviewer will accept, and no idea what's actually missing.

This happens constantly. NIST AI RMF 1.0 is voluntary — there's no certifying body, no exam, no badge. But it has become the default shared vocabulary between engineering, legal, procurement, and regulators in the US, because it's free, comprehensive, and (unlike a lot of compliance material) genuinely maps to real engineering practice. It's referenced by federal agencies implementing AI executive orders, cited in state-level AI legislation, and it's the document NIST used as the backbone for the official crosswalk NIST published between the RMF and ISO/IEC 42001 — the two are designed to interoperate, not compete.

The gap isn't understanding that the framework exists. It's translation: turning "establish policies and procedures for third-party risk" into a specific file in your repo, a specific CI check, a specific person who owns it. That translation is the actual job of an AI governance engineer, and it's what this lesson builds.

The Four Functions

NIST AI RMF 1.0 organizes AI risk management into four functions, each with several categories and subcategories detailed further in the companion RMF Playbook:

Function	What it covers	What "done" looks like
Govern	Culture, policy, accountability, and organizational structures for managing AI risk	A written risk policy, assigned roles (RACI), a maintained risk register, incident escalation paths
Map	Understanding the AI system's context: intended use, data, stakeholders, and known limitations	A system context doc, dataset cards, a completed threat model, documented out-of-scope uses
Measure	Analyzing and tracking AI risks with quantitative or qualitative metrics	An automated eval suite, red-team results tracked over time, fairness metrics, drift monitoring
Manage	Allocating resources to respond to mapped and measured risks	Deployment gates, rollback criteria, an incident response plan, decommissioning triggers

Notice the shape: Govern and Map are mostly about documentation and structure — they answer "do we know what we're building and who's accountable for it." Measure is about evidence — did we actually run the tests and what did they say. Manage is about action — what happens when the evidence says something is wrong. A team that's strong on Map but weak on Manage has done a great job describing its system and a terrible job deciding what to do when it breaks.

RMF and ISO 42001 are designed to interoperate, not compete
NIST published an official crosswalk mapping AI RMF subcategories to ISO/IEC 42001 controls. If you're building toward ISO 42001 certification (Lesson 3), doing your RMF self-assessment first isn't wasted work — most of the Govern and Map evidence you produce here (policies, risk register, system context docs) is reusable as ISO 42001 audit evidence. Treat RMF as the free, fast-moving first pass and ISO 42001 as the certifiable, auditable version of the same underlying controls.

A team has a one-page system context doc describing their AI system's intended use and out-of-scope uses, a dataset card for their RAG corpus, and a completed threat model. They have no written AI risk policy, no assigned RACI, and no risk register. Which function are they strong in, and which are they missing entirely?

System context, dataset cards, and threat models are all Map artifacts — they characterize the system and its context. A written risk policy, RACI, and a risk register are Govern artifacts — they establish organizational accountability. This team has done real technical characterization work but has no organizational structure around AI risk at all. The fix is organizational (write the policy, assign owners), not more system documentation.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Generative AI Profile: NIST-AI-600-1, Turning RMF Language into a Checklist, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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