AI Governance
AI governance is the framework of policies, roles, controls, and processes an organization uses to ensure its AI systems are lawful, safe, fair, accountable, and aligned with business intent — across the full lifecycle from problem framing to retirement.
Full definition
Effective AI governance maps to recognised frameworks — NIST AI RMF (US), ISO/IEC 42001:2023 (international AI management system standard), and the EU AI Act — and instantiates them as concrete artifacts: an AI inventory, risk classification per system, model cards, evaluation reports, deployment approval workflow, monitoring dashboards, and incident response. Governance is owned by a cross-functional body: legal, security, data, ML, and the business owner.
Why it matters
AI governance is the new SOX for the AI era. Boards now ask the same questions about AI controls that they asked about financial controls 20 years ago. Organizations without a documented governance program face regulatory exposure (EU AI Act fines reach 7% of global revenue), procurement disqualification, and unmanaged operational risk.
Example
A health insurer adopts ISO/IEC 42001 as its AI management system. Every model — claims-triage, fraud-detection, member-churn — is registered, classified by risk tier, evaluated, monitored, and reviewed annually by an internal AI risk committee.
Related terms
- EU AI ActThe EU AI Act (Regulation (EU) 2024/1689) is the European Union's comprehensive, risk-tiered regulation of AI systems, the world's first horizontal AI law, with obligations phasing in from February 2025 and full general-purpose AI rules applying from August 2025.
- AI AuditAn AI audit is a structured, evidence-based examination of an AI system or AI program against defined criteria — covering training data, model, deployment context, monitoring, and governance — performed by an internal team, an external firm, or a regulator.
- AI BiasAI bias is systematic, unfair difference in an AI system's outputs across demographic, geographic, or other groups — usually caused by biased training data, biased labels, or biased problem framing rather than the algorithm itself.
- Algorithmic AccountabilityAlgorithmic accountability is the principle that a specific person, role, or organization is identifiable and answerable for the design, deployment, outcomes, and harms of an automated decision system — and that the mechanisms to enforce that answerability exist.
- Explainability (XAI)Explainability (XAI) is the property of an AI system whose decisions can be understood by humans — through model-level documentation, prediction-level attributions, and counterfactual explanations — and a regulatory expectation in finance, healthcare, hiring, and other high-stakes domains.
Source & further reading
Primary source: ISO/IEC 42001:2023 — Artificial intelligence management system (2023).
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