AI Governance KPIs and Metrics
Measuring whether AI governance is actually working. Covers the metrics, monitoring frameworks, and reporting structures that boards and compliance teams use to move beyond policy documents and demonstrate that controls are operating effectively.
Key board-level questions
- 1.What metrics do we use to measure the effectiveness of our AI governance program?
- 2.How do we report AI risk to the board in a way that is actionable, not just informational?
- 3.Are we tracking model performance, drift, and incident rates in a consistent way?
- 4.How do we know our human oversight controls are working rather than just present on paper?
Regulatory frameworks
NIST Artificial Intelligence Risk Management Framework Playbook
The voluntary NIST AI RMF Playbook provides implementation guidance, suggested actions, and example outputs across AI use cases. It supports GOVERN, MAP, MEASURE, and MANAGE throughout the system lifecycle.
ISO/IEC 42001:2023 - Artificial Intelligence Management System
ISO and IEC published ISO/IEC 42001:2023 in December 2023 as the first international AI management system standard. It sets requirements for establishing, maintaining, and improving an AI Management System. Organizations developing or using AI products and services can seek independent certification.
NIST AI 600-1 Generative AI Profile
This companion to NIST AI RMF 1.0 addresses generative AI risks. It covers large language models and multimodal foundation models.
EU AI Act: AI Literacy and Prohibited AI Systems Provisions (Applicable 2 February 2026)
This entry lists February 2, 2026 as the EU AI Act’s first major compliance deadline. It requires AI developers and deployers in the EU to establish workforce literacy measures. It also describes enforceable prohibitions on unacceptable-risk practices. Organizations must cease prohibited practices and demonstrate adequate staff competency by that date.
Playbook guidance
What is our process for model drift monitoring?
How do we build and maintain an AI model registry?
What does audit-ready AI documentation look like in practice?
How do we apply a three lines of defense model to AI risk?
What does meaningful human oversight look like for high-risk AI decisions?
How do we ensure human-in-the-loop review is actually effective?