Foundation course
AI Governance Fundamentals
A practical foundation for understanding AI systems, accountability, lifecycle controls, global governance approaches, and evidence-based oversight.
Curriculum
From principle to operating evidence
Lessons use concise analysis and realistic cases. Enrolled learners can open the full material and record completion.
Governance foundations
Build a shared vocabulary for governing AI as a socio-technical system.
- What AI governance does · 25 minutes · Article
- Lifecycle and accountable actors · 25 minutes · Article
- Case study: the unowned recommender · 30 minutes · Case study
Risk and control
Translate use-case context into a proportionate control plan.
- Contextual risk assessment · 30 minutes · Article
- Selecting and layering controls · 30 minutes · Article
- Case study: a screening tool under pressure · 30 minutes · Case study
Global frameworks and evidence
Use laws, standards, and voluntary frameworks without confusing their authority.
- Authority and applicability · 30 minutes · Article
- Evidence and traceability · 30 minutes · Article
- Case study: framework shopping · 25 minutes · Case study
Operating governance
Run inventory, approval, monitoring, incident, and improvement processes as a coherent system.
- Inventory and approval gates · 30 minutes · Article
- Monitoring, incidents, and improvement · 30 minutes · Article
- Case study: drift after launch · 25 minutes · Case study
Learning outcomes
What you will be able to do
- Distinguish AI governance from ethics, compliance, cybersecurity, and model operations while explaining how they work together.
- Map accountable roles across the AI lifecycle and maintain a decision-useful AI system inventory.
- Classify use cases by context and impact without treating a single framework as universal law.
- Select proportionate controls for data, testing, transparency, human oversight, monitoring, and incident response.
- Evaluate governance evidence and identify unsupported assurance claims.