Implementing AI Governance in an Enterprise Environment

CIO/CDO office, AI/ML teams, management, enterprise architects, risk management, compliance, legal, cyber security, data governance and product teams.

After completing the course, you will be able to:
• design an AI governance operating model
• classify AI systems by risk
• create an AI inventory
• define governance roles and processes
• implement AI risk management
• prepare the organisation for AI compliance
• create a roadmap for introducing AI governance

You will take away:
• an AI governance framework
• an AI inventory template
• an AI risk assessment template
• an AI governance RACI model
• an AI policy checklist
• a roadmap for introducing AI governance

40% theory, 60% workshops and practical scenarios.

Day 1 – AI Governance Fundamentals and the AI Operating Model

1. Introduction to AI governance
• Why AI governance emerges
• Risks of GenAI and production AI systems
• AI governance vs data governance
• AI lifecycle management

2. AI governance framework
• Governance operating model
• Centralised vs federated governance
• AI Governance Council
• AI policies and standards

3. AI use cases and AI inventory
• Identifying AI systems
• AI inventory management
• Shadow AI
• Third-party AI systems

4. Workshop
• Identifying AI use cases in the organisation
• Designing an AI inventory
• Mapping AI systems

Day 2 – AI Risk Management and Compliance

1. AI risk management
• Risk taxonomy
• Bias and fairness
• Hallucinations
• Explainability
• Security risks
• Third-party risks

2. The EU AI Act in practice
• Risk-based classification
• High-risk AI systems
• Foundation models and GenAI
• Obligations of providers and deployers

3. AI controls and governance processes
• Human oversight
• Approval workflows
• AI incident management
• Monitoring and observability
• Auditability and logging

4. Workshop
• AI risk assessment
• Classifying AI systems under the AI Act
• Designing AI controls

Day 3 – Implementing AI Governance in Production

1. AI governance in enterprise architecture
• AI platform governance
• MLOps and LLMOps governance
• Model lifecycle management
• Vendor governance

2. AI governance processes
• Onboarding AI systems
• Approving AI use cases
• Model monitoring
• AI change management
• AI documentation

3. AI governance tooling
• Microsoft Purview
• MLflow
• LangSmith
• OpenMetadata
• Model cards
• AI risk registers

4. Introducing AI governance in the organisation
• Change management
• AI literacy programme
• Governance maturity assessment
• Pilot domain
• Rollout roadmap

5. Final workshop
• Designing an AI governance roadmap
• Governance operating model
• Defining KPIs and ownership

Previous courses

Úvod do Data Governance [GOC671]

Data Governance - Pokročilé strategické řízení dat v organizaci [GOC673]

Data Governance - Úvod do řízení dat v organizaci [GOC672]

EU AI Act in Practice [GOC675]