AI Governance Overview
A comprehensive overview of AI governance: frameworks, risk management, model documentation, audit obligations, and EU AI Act compliance requirements.
AI governance is the set of policies, processes, technical controls, and accountability structures that organizations put in place to ensure AI systems are developed and operated responsibly, safely, and in compliance with applicable regulation.
Effective AI governance addresses the full AI lifecycle: from data sourcing and model development, through deployment and monitoring, to retirement and audit.
As AI systems take on higher-stakes decisions — in credit, hiring, healthcare, law enforcement, and public services — governance frameworks have moved from voluntary best practice to regulatory obligation.
Core Governance Obligations
Organizations deploying AI systems must address: risk classification, training data documentation, model documentation, decision logging, audit trail maintenance, bias and fairness assessment, incident reporting, and ongoing monitoring. The EU AI Act codifies these obligations for high-risk AI systems into binding law.