What AI are we using?
Maintain visibility through an AI inventory or register.
AI governance foundations
What Is AI Governance?
AI governance is the practical system an organisation uses to decide how AI is selected, approved, used, monitored and controlled. It makes clear who is accountable, what rules apply, what risks must be managed and when human review is required.
Plain-English definition
Governance turns AI use into managed business practice.
AI governance covers structures, processes, policies, controls and accountabilities. It is not only a technology issue. It is a people, risk, ethics, privacy, cyber security, legal and leadership issue.
What could go wrong?
Assess privacy, cyber security, bias, safety, workforce and accountability risks.
Who is accountable?
Assign owners before AI is deployed or used in important workflows.
What it should cover
Seven parts of practical AI governance.
Strategy
How AI supports purpose, values and business goals.
Risk appetite
What AI use is acceptable, restricted or prohibited.
Accountability
Who approves, monitors and reviews AI systems.
Policies
Clear rules for tools, data, staff use and human review.
Risk controls
Controls for privacy, bias, cyber security, accuracy and workforce impact.
Human control
Meaningful review and intervention, especially for high-impact decisions.
Assurance
Testing, monitoring, audit, reporting and regular review.
Culture
AI literacy, worker consultation and responsible use habits.
Why AI is different
AI can be useful and hard to predict at the same time.
Traditional software usually follows rules that developers have explicitly programmed. AI systems often generate outputs by identifying patterns in data. That can make them powerful and scalable, but harder to test, explain and control without a governance system.