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 accuracy, changes in behavior over time, 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 AI Risk Management Framework (AI RMF 1.0) and Playbook
The NIST AI Risk Management Framework (AI RMF 1.0) is a voluntary US framework for managing the risks of AI systems. It organizes the work into four functions: Govern, Map, Measure, and Manage. Its companion Playbook suggests concrete actions for each part. Any organization that builds or uses AI can adopt it, and some laws and contracts point to it.
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 (general-purpose models that handle text, images, and audio).
EU AI Act (Regulation (EU) 2024/1689)
The EU AI Act is the European Union's law on artificial intelligence. It sorts AI systems by risk, bans a short list of practices, and sets duties for high-risk systems and general-purpose AI models. It applies to any organization that builds, sells, or uses AI in the EU, wherever that organization is based. Obligations phase in between February 2025 and August 2028.
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?