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
Voluntary, use-case-agnostic operational companion to the NIST AI Risk Management Framework (AI RMF 1.0) that provides structured, actionable guidance, suggested actions, and example outputs for implementing the four core AI RMF functions-GOVERN, MAP, MEASURE, and MANAGE-across the AI system lifecycle.
ISO/IEC 42001:2023 – Information Technology – Artificial Intelligence – Management System
The first internationally certified AI management system standard, providing a structured framework for establishing, implementing, maintaining, and continually improving an organization's AI management system (AIMS), with certifiable requirements applicable to any organization that develops, provides, or uses AI-based products and services.
NIST AI 600-1 Generative AI Profile
A companion resource to the NIST AI RMF 1.0 that provides structured guidance for managing the unique risks presented by generative AI systems, including large language models and multimodal foundation models.
EU AI Act: AI Literacy and Prohibited AI Systems Provisions (Applicable 2 February 2026)
The EU AI Act's first major compliance deadline takes effect on 2 February 2026, requiring all organizations that develop or deploy AI within the EU to establish AI literacy measures for their workforce. As of this date, the Act's prohibitions on AI systems deemed to pose unacceptable risks also become enforceable. Organizations must have ceased operation of any prohibited AI practices and demonstrated adequate staff competency with AI systems by this 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?