Definition
AI Governance
AI governance is the set of policies, controls and processes that guide how AI systems are built, deployed and monitored responsibly.
Short definition
AI governance is the discipline of managing AI systems responsibly. It covers policies, risk assessments, documentation, model evaluation, privacy controls, security, monitoring and accountability.
How it works
Organizations define which AI uses are allowed, who approves them, what data can be used, how outputs are reviewed and how incidents are handled. Governance should match risk: a brainstorming tool needs less control than a system affecting finance, health or employment.
Example
A company may require teams to document data sources, test hallucination rates, log model usage and add human approval for external customer communication.
Why it matters
AI governance helps companies move faster without losing control. It reduces legal, security and reputational risk, and it gives teams a clear path for deploying AI features responsibly.
Governance is more than documentation
An effective program connects policy with technical controls. It may include an AI inventory, a named business owner, risk classification, access control, pre-release evaluation, monitoring and a procedure for disabling a system. A policy document without accountable owners or enforcement quickly becomes irrelevant.
Controls should match the risk
An internal brainstorming assistant does not need the same controls as a model influencing credit, employment or healthcare. Good governance does not stop every experiment. It allows low-risk uses to move quickly while requiring stronger testing, human oversight and audit trails when a decision can seriously affect a person.