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The AI Governance Policy Framework.

A committee is not governance. Governance is the set of bodies, decision rights, controls, and gates that decide what ships and what gets stopped. This framework gives you the operating model and the policy template to run it.

In depth

A Committee That Cannot Stop a Launch Is Theater.

01

The common pattern: a review board that meets monthly while product teams deploy without its sign-off, so the board is advisory and the governance is fictional.

02

The approach that works: decision rights written down and enforced, controls scaled to risk, and an approval gate wired into the deployment pipeline.

The detail

The Three Moves Every Chief AI Officer Needs.

Zone · 01

Write Down Who Can Say No

Someone has to hold the authority to approve or block a deployment, and that authority has to be written down and respected.

Zone · 02

Tier Every System Before It Is Built

Tier each AI system by two things: the risk if it gets the answer wrong, and how much autonomy it has to act without a human.

Zone · 03

Put the Gate in the Pipeline

A policy that lives in a document is a suggestion. Make the model approval gate a required step to deploy.

By the numbers

The figures that make it a board-level conversation.

78%
Share of organizations that reported using AI in at least one business function in 2024
47%
Share of organizations that have experienced at least one negative consequence from generative AI
$4.88M
Average cost of a data breach in 2024, the highest on record
Inside the report

What you'll take away.

01

The Governance Operating Model

Four bodies carry the function: the AI oversight committee that sets policy and holds final decision rights.

02

The Risk-Tiering Model

Four tiers from minimal to autonomous, scored on stakes and autonomy.

03

The Policy Components

The seven components every system answers to, scaled by tier.

04

The Mapping and Readiness Checklist

A direct map from this model to the NIST AI RMF Govern, Map, Measure, and Manage functions.

Questions

Frequently asked.

Who is this framework for?

Chief AI Officers and the AI governance function: the people responsible for deciding what an AI system is allowed to do, who signs off, and what stops it when something goes wrong. It also serves CISOs, CDOs, and legal and compliance leads who sit on the oversight bodies.

What is the difference between a committee and AI governance?

A committee meets and documents. Governance has three properties a committee usually lacks: decision rights that are written down and enforced, controls tied to risk and autonomy, and an approval gate that is a required step in the pipeline rather than a meeting teams route around.

How does the risk-tiering model work?

Every AI system is scored at intake on two axes: the risk if it gets the answer wrong, and how much autonomy it has to act without a human. That score assigns one of four tiers, and the tier sets which body reviews the system, which controls apply, and how hard the approval gate is. If a system's use changes, the tier is reviewed.

Does this framework align with NIST AI RMF and ISO/IEC 42001?

Yes. The framework maps directly onto both. The oversight committee and accountability structure align to the NIST AI RMF Govern function and ISO/IEC 42001 leadership and roles clauses. Risk tiering maps to Map, evaluation gates to Measure, and monitoring and incident response to Manage. Build to this model and you build to both standards.

Is this a policy document or an operating model?

Both. The framework is a usable template, not a read. It gives you the bodies and roles for the operating model, the tiering model with controls per tier, the policy components written so you can adapt them, the standards mapping, and a readiness checklist. Use it to stand up governance, not to study it.

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Next step

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