AI governance

What is AI governance?

AI governance is the set of policies, controls, and oversight that keep an organization’s use of artificial intelligence safe, fair, transparent, and accountable. Emerging standards include ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act.

Definition

AI governance is how an organization directs and controls its development and use of artificial intelligence: the policies, roles, risk assessments, and monitoring that make AI use responsible and accountable.

Background

As AI moves into products and operations, regulators and customers increasingly expect it to be governed like any other high-impact technology. Three references anchor the emerging practice: ISO/IEC 42001:2023, the first management-system standard for AI; the NIST AI Risk Management Framework (AI RMF 1.0), a voluntary framework organized around the functions Govern, Map, Measure, and Manage; and the EU AI Act, a risk-tiered regulation that places obligations on AI systems based on how much risk they pose.

Why it matters

AI introduces new risks, bias, opacity, data misuse, and unreliable output, that existing controls don’t fully cover. Governing AI protects your customers and your business, and it is quickly becoming a procurement and regulatory requirement rather than a nice-to-have.

Step by step

1. Inventory where AI is used, built, or bought across the organization.
2. Assess each use for risk: impact on people, data sensitivity, and how autonomous it is.
3. Set policies for acceptable use, human oversight, data handling, and transparency.
4. Assign accountability and a review process for higher-risk systems.
5. Monitor AI systems in production for drift, misuse, and unexpected behavior.
6. Align to a recognized framework (ISO/IEC 42001, NIST AI RMF) and, where in scope, the EU AI Act.

Examples

• A company adds an AI acceptable-use policy and a lightweight risk review before any team ships an AI feature.
• A vendor mapping its program to ISO/IEC 42001 documents human-oversight and data-governance controls for its models.

Common mistakes

• Treating AI governance as purely a legal exercise instead of an operational control set.
• Governing only models you build while ignoring third-party AI tools employees adopt.
• Writing a policy but never monitoring AI systems once they are live.

FAQ