AI Governance

Course subjects

Introduction to AI Governance

• What AI governance is and what it is not

• Scope and importance of governance in AI use

• Organisational accountability and responsibility

• Why most AI failures are governance failures

Decision Authority and Accountability

• Who approves AI use cases

• Human oversight requirements

• Governance differences between government and enterprise

• AI governance across the full lifecycle

AI Risk Management

• Bias, model drift, data leakage, and operational risk

• Embedding controls across AI use

• Governed versus ungoverned AI outcomes

Transparency and Assurance

• Explainability and defensibility of AI decisions

• Audit trails and documentation

• Transparency expectations by sector

Regulatory and Policy Alignment

• Government AI governance obligations

• Enterprise regulatory considerations

• International standards and compliance expectations

Data Governance for AI

• Data classification and provenance

• Retention and deletion considerations

• Privacy-by-design for AI

Operating AI Responsibly

• Integration with IT and service management

• Incident and change management

• Clear accountability when issues arise

Practical Adoption and Value

• Minimum viable AI governance

• Risk-proportionate governance approaches

• Governance as an enabler of innovation

Skills and Workforce Expectations

• AI literacy and safe-use training

• Technical, risk, communication, and ethical skills

Wrap-Up and Key Takeaways

• Accountability as the foundation of AI governance

• Governance as an ongoing organisational capability