AI governance is the set of frameworks, policies, and operational controls that define how an organization builds, deploys, and monitors AI systems. It addresses questions of accountability, transparency, risk, and compliance: Who is responsible when an AI model makes a wrong decision? How is model behavior monitored in production? What happens when a model drifts or produces harmful output?
Governance becomes critical once AI moves beyond experimentation and into production systems that affect real users or business outcomes. Boards and executive teams are increasingly being asked to demonstrate AI oversight by regulators, investors, and enterprise customers. In regulated industries like healthcare and financial services, the absence of documented AI governance is itself a compliance risk.
A practical AI governance framework typically covers model documentation, risk classification, human-in-the-loop requirements, audit logging, bias evaluation, and escalation procedures. It also defines what decisions an AI system is and is not permitted to make autonomously, and what thresholds trigger human review.
Governance is not just about risk reduction. Organizations with clear AI governance frameworks move faster, not slower, because teams have pre-approved patterns to follow rather than making risk decisions on a case-by-case basis at the engineer level. The goal is AI that is owned, auditable, and defensible at the board level.