What you'll learn:
- Build a complete AI governance framework from scratch using the 6 pillars: accountability, risk, transparency, fairness, privacy, and compliance
- Classify any AI system into EU AI Act risk tiers (unacceptable, high, limited, minimal) and assign the right controls for each level
- Run a full AI risk assessment covering bias and fairness audits, data privacy risks, and third-party vendor due diligence
- Write production-ready governance artefacts: model cards, acceptable use policy, incident response runbook, and audit trails
- Govern generative AI specifically through prompt governance, output validation, and cost controls for LLM APIs
- Plan a realistic 90-day implementation roadmap and get buy-in from leadership, legal, and engineering
- Apply everything to a real capstone: build an AI Governance Scorecard for a fictional retailer end to end
This course contains the use of artificial intelligence.
AI is everywhere in your organization. Governance usually isn't.
The EU AI Act is in force. GDPR Article 22 still applies. Your CFO wants to know how much you're spending on LLM APIs. Your legal team is asking who signs off on the new AI hiring tool. And someone in marketing just pasted a client list into a chatbot.
This course gives you a complete, working AI governance framework you can apply in your organization immediately. No theory-heavy slides. No vague principles. Just the structures, policies, and artefacts that real governance programs run on.
What you will build
In three hours, you'll work through 30 focused lectures organized into 7 sections. You'll go from "why governance matters" to writing your own AI Model Card, Acceptable Use Policy, Incident Response runbook, and a 90-day implementation roadmap. Then you'll apply everything in a capstone lab: a full governance scorecard for a fictional retailer with five AI systems across three departments.
What's inside
The 6 pillars of AI governance and how to score against them
EU AI Act risk classification, GDPR overlap, and US frameworks (NIST AI RMF, Colorado AI Act)
A repeatable risk assessment process for any AI system
Bias auditing, privacy risks, and third-party vendor due diligence
Generative AI specifics: prompt governance, output validation, cost controls
Downloadable templates you can drop into your own organization
Hands-on labs and a scorecard you keep as a portfolio piece
This course contains a promotion.
