AI Governance Track · Governance · Responsible AI · Leadership

AI Governance & Responsible AI

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A complete AI governance and responsible-AI certification program - 559 chapters across six phases, from literacy to mastery, for the leaders who own AI risk.

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6
Phases
559
Chapters
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program
Board-ready
Capstones
About the course

What it covers and how it works

A comprehensive, self-paced certification program that builds the complete AI governance practitioner - from first principles to frontier practice. It spans six phases and 559 chapters: AI governance literacy, the rulebook of laws and frameworks, the controls that manage AI risk across the lifecycle, the operations that run governance at scale, the leadership that influences the enterprise, and the mastery topics of the complete practitioner.

The six phases move from literacy, through the rulebook of laws and frameworks and the controls that manage AI risk, into the operations and leadership of running the function - ending in the frontier topics of the complete practitioner.

Who it's for

The leaders who own AI risk

AI governance leads, chief-data and AI-risk officers, model-risk and compliance leaders, and the practitioners building the governance function. It scales from literacy for newcomers to mastery for those leading the enterprise practice.

Frameworks covered

Mapped to real regulation

The program operationalizes the EU AI Act, NIST AI RMF, ISO 42001, and sector rules, teaching the crosswalk technique that satisfies many regimes with one defensible control set - turning fragmentation into a single board-ready posture.

Curriculum

6 phases · 49 modules · 559 chapters

Modules are the structure; phases are the learning flow. Numbering is global and continuous from 1 to 559 with no repeated chapters. Expand any module for its full chapter list.

P1 Literacy 6 modules · 25 chapters · Ch 1–25

M01 Foundations of AI Governance Ch 1–6
  1. What Is AI Governance
  2. Why AI Governance Matters in 2025+
  3. Enterprise Risks from AI
  4. Key Roles & Stakeholders
  5. The Governance Operating Model
  6. Building an AI Inventory
M02 Understanding AI & Its Risks Ch 7–10
  1. The Language of AI for Governance Leaders
  2. Types of AI Systems and Why They Matter
  3. The AI Lifecycle End to End
  4. Data as the Foundation of AI Risk
M03 Principles, Ethics & Stakeholders Ch 11–15
  1. Responsible AI Principles and Where They Come From
  2. AI Ethics vs AI Governance vs AI Compliance
  3. Stakeholders Beyond the Enterprise
  4. The Business Value of Good Governance
  5. Common AI Governance Myths
M04 The Governance Practice & Value Ch 16–19
  1. A Day in the Life of an AI Governance Leader
  2. Maturity Models for AI Governance
  3. The Cost of Getting It Wrong
  4. Building AI Literacy Across the Organization
M05 Trust, Appetite & Tools Ch 20–23
  1. Trust, Transparency, and the Social License
  2. Introduction to AI Risk Appetite
  3. The Governance Leader's Toolkit Overview
  4. Reading and Interpreting AI Regulation
M06 Foundations Capstone Ch 24–25
  1. Case Studies in AI Governance Foundations
  2. Phase 1 Capstone - The Literacy Brief

P2 The Rulebook 10 modules · 55 chapters · Ch 26–80

M07 The Regulatory Map & Core Frameworks Ch 26–29
  1. The Global AI Regulatory Landscape
  2. The EU AI Act in Depth
  3. The NIST AI Risk Management Framework
  4. ISO/IEC 42001
M08 Sector & Data Regulation Ch 30–34
  1. Sector-Specific AI Regulation: Finance
  2. Sector-Specific AI Regulation: Healthcare
  3. Sector-Specific AI Regulation: Employment & HR
  4. Data Protection and AI: The GDPR Intersection
  5. Automated Decision-Making Rights
M09 Rights, Disclosure & Liability Ch 35–36
  1. Transparency and Disclosure Obligations
  2. AI Liability and Accountability Regimes
M10 The Jurisdictional Tour Ch 37–39
  1. The UK Approach to AI Regulation
  2. The US Federal and State Patchwork
  3. Asia-Pacific AI Frameworks
M11 Crosswalk & Synthesis Ch 40–40
  1. Building a Regulatory Crosswalk
M12 Compliance Mapping & Adjacent Domains Ch 41–50
  1. Intellectual Property and AI
  2. AI and Competition Law
  3. Consumer Protection and AI
  4. Sector Deep Dive: Insurance
  5. Sector Deep Dive: Public Sector & Government
  6. Sector Deep Dive: Critical Infrastructure
  7. Biometrics and Facial Recognition Law
  8. Children and Vulnerable Groups
  9. AI in Marketing and Advertising
  10. Content Moderation and Platform Rules
M13 Standards, Assurance & Enforcement Ch 51–58
  1. Open Source and AI Governance
  2. Foundation Models and General-Purpose AI Rules
  3. Cross-Border Data Flows and AI
  4. Standards Bodies and the Standardization Landscape
  5. Certification, Audit, and Conformity Ecosystems
  6. Voluntary Codes and Industry Commitments
  7. Regulatory Sandboxes and Innovation Pathways
  8. Enforcement, Penalties, and Regulatory Actions
M14 Engagement, Deals & Documentation Ch 59–63
  1. Horizon Scanning for New Regulation
  2. Negotiating AI Clauses in Contracts
  3. Regulatory Relationships and Engagement
  4. AI Governance in Mergers and Acquisitions
  5. Records, Documentation, and Evidentiary Duties
M15 Advanced Regulatory Strategy Ch 64–79
  1. The Politics and Economics of AI Regulation
  2. Comparative Regulatory Philosophies
  3. AI and Sector Convergence Risks
  4. Emerging Liability for Autonomous Systems
  5. Whistleblowing and Internal Reporting Duties
  6. Regulatory Reporting and Notifications
  7. AI Governance and Financial Disclosure
  8. National Security and Export Controls
  9. Accessibility and Inclusive-Design Duties
  10. Environmental and Sustainability Obligations
  11. Litigation and Case Law Trends
  12. Regulatory Interpretation and Legal Opinions
  13. Multi-Jurisdiction Program Design
  14. AI Governance Frameworks Compared
  15. The Rulebook in Practice: An Integrated Case
  16. Future Regulatory Directions
M16 Phase 2 Capstone Ch 80–80
  1. Phase 2 Capstone - The Regulatory Strategy

P3 The Controls 9 modules · 120 chapters · Ch 81–200

M17 Risk Assessment & Tiering Ch 81–84
  1. AI Risk Assessment Fundamentals
  2. Building a Risk Taxonomy
  3. Risk Scoring and Tiering Methods
  4. Impact Assessments for AI
M18 Fairness & Bias Controls Ch 85–88
  1. Bias and Fairness: Concepts
  2. Fairness Metrics in Practice
  3. Bias Testing and Auditing
  4. Bias Mitigation Techniques
M19 Explainability & Transparency Ch 89–91
  1. Explainability: Concepts and Stakes
  2. Explainability Techniques for Leaders
  3. Transparency by Design
M20 Privacy, Data & Security Controls Ch 92–96
  1. Privacy-Preserving AI Techniques
  2. Data Governance Controls for AI
  3. Security of AI Systems: Threat Landscape
  4. Adversarial Robustness Controls
  5. Securing Generative and LLM Systems
M21 Oversight, Validation & Monitoring Ch 97–103
  1. Human Oversight Design
  2. Human-in-the-Loop Patterns
  3. Model Validation and Testing
  4. Documentation and Model Cards
  5. Monitoring AI in Production
  6. Drift Detection and Response
  7. Incident Detection and Classification
M22 Third-Party, Selection & Assurance Ch 104–110
  1. Third-Party and Vendor Risk Controls
  2. Supply-Chain and Foundation-Model Risk
  3. Control Selection and Design
  4. Control Testing and Assurance
  5. Residual Risk and Acceptance
  6. Red-Teaming AI Systems
  7. Safety Engineering for AI
M23 Controls - Advanced Track Ch 111–140
  1. AI Risk Assessment Fundamentals - Advanced
  2. Building a Risk Taxonomy - Advanced
  3. Risk Scoring and Tiering Methods - Advanced
  4. Impact Assessments for AI - Advanced
  5. Bias and Fairness: Concepts - Advanced
  6. Fairness Metrics in Practice - Advanced
  7. Bias Testing and Auditing - Advanced
  8. Bias Mitigation Techniques - Advanced
  9. Explainability: Concepts and Stakes - Advanced
  10. Explainability Techniques for Leaders - Advanced
  11. Transparency by Design - Advanced
  12. Privacy-Preserving AI Techniques - Advanced
  13. Data Governance Controls for AI - Advanced
  14. Security of AI Systems: Threat Landscape - Advanced
  15. Adversarial Robustness Controls - Advanced
  16. Securing Generative and LLM Systems - Advanced
  17. Human Oversight Design - Advanced
  18. Human-in-the-Loop Patterns - Advanced
  19. Model Validation and Testing - Advanced
  20. Documentation and Model Cards - Advanced
  21. Monitoring AI in Production - Advanced
  22. Drift Detection and Response - Advanced
  23. Incident Detection and Classification - Advanced
  24. Third-Party and Vendor Risk Controls - Advanced
  25. Supply-Chain and Foundation-Model Risk - Advanced
  26. Control Selection and Design - Advanced
  27. Control Testing and Assurance - Advanced
  28. Residual Risk and Acceptance - Advanced
  29. Red-Teaming AI Systems - Advanced
  30. Safety Engineering for AI - Advanced
M24 Controls - Applied Track Ch 141–170
  1. AI Risk Assessment Fundamentals - Applied
  2. Building a Risk Taxonomy - Applied
  3. Risk Scoring and Tiering Methods - Applied
  4. Impact Assessments for AI - Applied
  5. Bias and Fairness: Concepts - Applied
  6. Fairness Metrics in Practice - Applied
  7. Bias Testing and Auditing - Applied
  8. Bias Mitigation Techniques - Applied
  9. Explainability: Concepts and Stakes - Applied
  10. Explainability Techniques for Leaders - Applied
  11. Transparency by Design - Applied
  12. Privacy-Preserving AI Techniques - Applied
  13. Data Governance Controls for AI - Applied
  14. Security of AI Systems: Threat Landscape - Applied
  15. Adversarial Robustness Controls - Applied
  16. Securing Generative and LLM Systems - Applied
  17. Human Oversight Design - Applied
  18. Human-in-the-Loop Patterns - Applied
  19. Model Validation and Testing - Applied
  20. Documentation and Model Cards - Applied
  21. Monitoring AI in Production - Applied
  22. Drift Detection and Response - Applied
  23. Incident Detection and Classification - Applied
  24. Third-Party and Vendor Risk Controls - Applied
  25. Supply-Chain and Foundation-Model Risk - Applied
  26. Control Selection and Design - Applied
  27. Control Testing and Assurance - Applied
  28. Residual Risk and Acceptance - Applied
  29. Red-Teaming AI Systems - Applied
  30. Safety Engineering for AI - Applied
M25 Controls - Deep Dive Track Ch 171–200
  1. AI Risk Assessment Fundamentals - Deep Dive
  2. Building a Risk Taxonomy - Deep Dive
  3. Risk Scoring and Tiering Methods - Deep Dive
  4. Impact Assessments for AI - Deep Dive
  5. Bias and Fairness: Concepts - Deep Dive
  6. Fairness Metrics in Practice - Deep Dive
  7. Bias Testing and Auditing - Deep Dive
  8. Bias Mitigation Techniques - Deep Dive
  9. Explainability: Concepts and Stakes - Deep Dive
  10. Explainability Techniques for Leaders - Deep Dive
  11. Transparency by Design - Deep Dive
  12. Privacy-Preserving AI Techniques - Deep Dive
  13. Data Governance Controls for AI - Deep Dive
  14. Security of AI Systems: Threat Landscape - Deep Dive
  15. Adversarial Robustness Controls - Deep Dive
  16. Securing Generative and LLM Systems - Deep Dive
  17. Human Oversight Design - Deep Dive
  18. Human-in-the-Loop Patterns - Deep Dive
  19. Model Validation and Testing - Deep Dive
  20. Documentation and Model Cards - Deep Dive
  21. Monitoring AI in Production - Deep Dive
  22. Drift Detection and Response - Deep Dive
  23. Incident Detection and Classification - Deep Dive
  24. Third-Party and Vendor Risk Controls - Deep Dive
  25. Supply-Chain and Foundation-Model Risk - Deep Dive
  26. Control Selection and Design - Deep Dive
  27. Control Testing and Assurance - Deep Dive
  28. Residual Risk and Acceptance - Deep Dive
  29. Red-Teaming AI Systems - Deep Dive
  30. Safety Engineering for AI - Deep Dive

P4 Operations 9 modules · 130 chapters · Ch 201–330

M26 The Operating Lifecycle & Intake Ch 201–206
  1. The AI Use-Case Lifecycle in Operation
  2. Designing the Intake Process
  3. Running an AI Governance Committee
  4. Decision Rights and Escalation in Practice
  5. Governance Tooling and the GRC Stack
  6. Metrics, KPIs, and Dashboards
M27 Reporting, Audit & Policy Ch 207–210
  1. Board and Executive Reporting
  2. Audit Readiness and Evidence Management
  3. Policy and Standard Authoring
  4. Training and Culture Change
M28 Incident, Scaling & SDLC Ch 211–216
  1. AI Incident Management End to End
  2. Post-Incident Review and Learning
  3. Scaling Governance Across the Enterprise
  4. Embedding Governance in the SDLC
  5. Governance for Procurement and Vendors
  6. Change Management for AI Systems
M29 Rhythm, Coordination & Automation Ch 217–224
  1. The Governance Operating Rhythm
  2. Cross-Functional Coordination
  3. Self-Service and Delegated Governance
  4. Governance Workflow Automation
  5. Managing the Risk Register
  6. Continuous Compliance Monitoring
  7. Governance for Rapid Experimentation
  8. Stakeholder Communication and Engagement
M30 Resourcing, Docs & Oversight Ch 225–230
  1. Resourcing and Capacity Planning
  2. Governance Documentation Systems
  3. Operationalizing Human Oversight
  4. Managing Model Inventories at Scale
  5. Service-Level and Performance Management
  6. Operations Capstone - Running the Function
M31 Operations - Advanced Track Ch 231–260
  1. The AI Use-Case Lifecycle in Operation - Advanced
  2. Designing the Intake Process - Advanced
  3. Running an AI Governance Committee - Advanced
  4. Decision Rights and Escalation in Practice - Advanced
  5. Governance Tooling and the GRC Stack - Advanced
  6. Metrics, KPIs, and Dashboards - Advanced
  7. Board and Executive Reporting - Advanced
  8. Audit Readiness and Evidence Management - Advanced
  9. Policy and Standard Authoring - Advanced
  10. Training and Culture Change - Advanced
  11. AI Incident Management End to End - Advanced
  12. Post-Incident Review and Learning - Advanced
  13. Scaling Governance Across the Enterprise - Advanced
  14. Embedding Governance in the SDLC - Advanced
  15. Governance for Procurement and Vendors - Advanced
  16. Change Management for AI Systems - Advanced
  17. The Governance Operating Rhythm - Advanced
  18. Cross-Functional Coordination - Advanced
  19. Self-Service and Delegated Governance - Advanced
  20. Governance Workflow Automation - Advanced
  21. Managing the Risk Register - Advanced
  22. Continuous Compliance Monitoring - Advanced
  23. Governance for Rapid Experimentation - Advanced
  24. Stakeholder Communication and Engagement - Advanced
  25. Resourcing and Capacity Planning - Advanced
  26. Governance Documentation Systems - Advanced
  27. Operationalizing Human Oversight - Advanced
  28. Managing Model Inventories at Scale - Advanced
  29. Service-Level and Performance Management - Advanced
  30. Operations Capstone - Running the Function - Advanced
M32 Operations - Applied Track Ch 261–290
  1. The AI Use-Case Lifecycle in Operation - Applied
  2. Designing the Intake Process - Applied
  3. Running an AI Governance Committee - Applied
  4. Decision Rights and Escalation in Practice - Applied
  5. Governance Tooling and the GRC Stack - Applied
  6. Metrics, KPIs, and Dashboards - Applied
  7. Board and Executive Reporting - Applied
  8. Audit Readiness and Evidence Management - Applied
  9. Policy and Standard Authoring - Applied
  10. Training and Culture Change - Applied
  11. AI Incident Management End to End - Applied
  12. Post-Incident Review and Learning - Applied
  13. Scaling Governance Across the Enterprise - Applied
  14. Embedding Governance in the SDLC - Applied
  15. Governance for Procurement and Vendors - Applied
  16. Change Management for AI Systems - Applied
  17. The Governance Operating Rhythm - Applied
  18. Cross-Functional Coordination - Applied
  19. Self-Service and Delegated Governance - Applied
  20. Governance Workflow Automation - Applied
  21. Managing the Risk Register - Applied
  22. Continuous Compliance Monitoring - Applied
  23. Governance for Rapid Experimentation - Applied
  24. Stakeholder Communication and Engagement - Applied
  25. Resourcing and Capacity Planning - Applied
  26. Governance Documentation Systems - Applied
  27. Operationalizing Human Oversight - Applied
  28. Managing Model Inventories at Scale - Applied
  29. Service-Level and Performance Management - Applied
  30. Operations Capstone - Running the Function - Applied
M33 Operations - Deep Dive Track Ch 291–320
  1. The AI Use-Case Lifecycle in Operation - Deep Dive
  2. Designing the Intake Process - Deep Dive
  3. Running an AI Governance Committee - Deep Dive
  4. Decision Rights and Escalation in Practice - Deep Dive
  5. Governance Tooling and the GRC Stack - Deep Dive
  6. Metrics, KPIs, and Dashboards - Deep Dive
  7. Board and Executive Reporting - Deep Dive
  8. Audit Readiness and Evidence Management - Deep Dive
  9. Policy and Standard Authoring - Deep Dive
  10. Training and Culture Change - Deep Dive
  11. AI Incident Management End to End - Deep Dive
  12. Post-Incident Review and Learning - Deep Dive
  13. Scaling Governance Across the Enterprise - Deep Dive
  14. Embedding Governance in the SDLC - Deep Dive
  15. Governance for Procurement and Vendors - Deep Dive
  16. Change Management for AI Systems - Deep Dive
  17. The Governance Operating Rhythm - Deep Dive
  18. Cross-Functional Coordination - Deep Dive
  19. Self-Service and Delegated Governance - Deep Dive
  20. Governance Workflow Automation - Deep Dive
  21. Managing the Risk Register - Deep Dive
  22. Continuous Compliance Monitoring - Deep Dive
  23. Governance for Rapid Experimentation - Deep Dive
  24. Stakeholder Communication and Engagement - Deep Dive
  25. Resourcing and Capacity Planning - Deep Dive
  26. Governance Documentation Systems - Deep Dive
  27. Operationalizing Human Oversight - Deep Dive
  28. Managing Model Inventories at Scale - Deep Dive
  29. Service-Level and Performance Management - Deep Dive
  30. Operations Capstone - Running the Function - Deep Dive
M34 Operations - In Practice Track Ch 321–330
  1. The AI Use-Case Lifecycle in Operation - In Practice
  2. Designing the Intake Process - In Practice
  3. Running an AI Governance Committee - In Practice
  4. Decision Rights and Escalation in Practice - In Practice
  5. Governance Tooling and the GRC Stack - In Practice
  6. Metrics, KPIs, and Dashboards - In Practice
  7. Board and Executive Reporting - In Practice
  8. Audit Readiness and Evidence Management - In Practice
  9. Policy and Standard Authoring - In Practice
  10. Training and Culture Change - In Practice

P5 Leadership 7 modules · 110 chapters · Ch 331–440

M35 Mandate, Business Case & Influence Ch 331–336
  1. Building the Governance Team
  2. Securing Mandate and Sponsorship
  3. Building the Business Case
  4. Influencing Without Authority
  5. Board and C-Suite Communication
  6. Avoiding the Department of No
M36 Strategy, Maturity & Crisis Ch 337–342
  1. Governance Strategy and Roadmaps
  2. Maturity Assessment and Improvement
  3. Vendor and Partner Management
  4. Crisis Leadership When AI Fails
  5. Navigating Organizational Resistance
  6. The Governance Leader's Career Path
M37 Stakeholders, Culture & Change Ch 343–350
  1. Stakeholder Mapping and Power
  2. Communicating Risk to Non-Experts
  3. Building a Governance Culture
  4. Leading Change at Scale
  5. Negotiation and Conflict Resolution
  6. Ethics Leadership and Moral Courage
  7. Strategic Alignment with the Business
  8. Measuring Leadership Effectiveness
M38 Talent, Budget & Effectiveness Ch 351–360
  1. Talent, Skills, and Development
  2. Governance and Innovation Partnership
  3. Budgeting and Resource Advocacy
  4. Executive Decision Support
  5. Reputation and Trust Leadership
  6. Leading Distributed and Global Teams
  7. Personal Effectiveness and Resilience
  8. Mentoring and Building the Profession
  9. Governance in the Executive Team
  10. Leadership Capstone - The Governance Leader
M39 Leadership - Advanced Track Ch 361–390
  1. Building the Governance Team - Advanced
  2. Securing Mandate and Sponsorship - Advanced
  3. Building the Business Case - Advanced
  4. Influencing Without Authority - Advanced
  5. Board and C-Suite Communication - Advanced
  6. Avoiding the Department of No - Advanced
  7. Governance Strategy and Roadmaps - Advanced
  8. Maturity Assessment and Improvement - Advanced
  9. Vendor and Partner Management - Advanced
  10. Crisis Leadership When AI Fails - Advanced
  11. Navigating Organizational Resistance - Advanced
  12. The Governance Leader's Career Path - Advanced
  13. Stakeholder Mapping and Power - Advanced
  14. Communicating Risk to Non-Experts - Advanced
  15. Building a Governance Culture - Advanced
  16. Leading Change at Scale - Advanced
  17. Negotiation and Conflict Resolution - Advanced
  18. Ethics Leadership and Moral Courage - Advanced
  19. Strategic Alignment with the Business - Advanced
  20. Measuring Leadership Effectiveness - Advanced
  21. Talent, Skills, and Development - Advanced
  22. Governance and Innovation Partnership - Advanced
  23. Budgeting and Resource Advocacy - Advanced
  24. Executive Decision Support - Advanced
  25. Reputation and Trust Leadership - Advanced
  26. Leading Distributed and Global Teams - Advanced
  27. Personal Effectiveness and Resilience - Advanced
  28. Mentoring and Building the Profession - Advanced
  29. Governance in the Executive Team - Advanced
  30. Leadership Capstone - The Governance Leader - Advanced
M40 Leadership - Applied Track Ch 391–420
  1. Building the Governance Team - Applied
  2. Securing Mandate and Sponsorship - Applied
  3. Building the Business Case - Applied
  4. Influencing Without Authority - Applied
  5. Board and C-Suite Communication - Applied
  6. Avoiding the Department of No - Applied
  7. Governance Strategy and Roadmaps - Applied
  8. Maturity Assessment and Improvement - Applied
  9. Vendor and Partner Management - Applied
  10. Crisis Leadership When AI Fails - Applied
  11. Navigating Organizational Resistance - Applied
  12. The Governance Leader's Career Path - Applied
  13. Stakeholder Mapping and Power - Applied
  14. Communicating Risk to Non-Experts - Applied
  15. Building a Governance Culture - Applied
  16. Leading Change at Scale - Applied
  17. Negotiation and Conflict Resolution - Applied
  18. Ethics Leadership and Moral Courage - Applied
  19. Strategic Alignment with the Business - Applied
  20. Measuring Leadership Effectiveness - Applied
  21. Talent, Skills, and Development - Applied
  22. Governance and Innovation Partnership - Applied
  23. Budgeting and Resource Advocacy - Applied
  24. Executive Decision Support - Applied
  25. Reputation and Trust Leadership - Applied
  26. Leading Distributed and Global Teams - Applied
  27. Personal Effectiveness and Resilience - Applied
  28. Mentoring and Building the Profession - Applied
  29. Governance in the Executive Team - Applied
  30. Leadership Capstone - The Governance Leader - Applied
M41 Leadership - Deep Dive Track Ch 421–440
  1. Building the Governance Team - Deep Dive
  2. Securing Mandate and Sponsorship - Deep Dive
  3. Building the Business Case - Deep Dive
  4. Influencing Without Authority - Deep Dive
  5. Board and C-Suite Communication - Deep Dive
  6. Avoiding the Department of No - Deep Dive
  7. Governance Strategy and Roadmaps - Deep Dive
  8. Maturity Assessment and Improvement - Deep Dive
  9. Vendor and Partner Management - Deep Dive
  10. Crisis Leadership When AI Fails - Deep Dive
  11. Navigating Organizational Resistance - Deep Dive
  12. The Governance Leader's Career Path - Deep Dive
  13. Stakeholder Mapping and Power - Deep Dive
  14. Communicating Risk to Non-Experts - Deep Dive
  15. Building a Governance Culture - Deep Dive
  16. Leading Change at Scale - Deep Dive
  17. Negotiation and Conflict Resolution - Deep Dive
  18. Ethics Leadership and Moral Courage - Deep Dive
  19. Strategic Alignment with the Business - Deep Dive
  20. Measuring Leadership Effectiveness - Deep Dive

P6 Mastery 8 modules · 119 chapters · Ch 441–559

M42 Frontier & Generative Systems Ch 441–446
  1. Governing Generative AI
  2. Governing Foundation Models
  3. Agentic AI and Autonomous Systems
  4. Frontier Models and Systemic Risk
  5. Multi-Agent and Compound Systems
  6. AI Governance in High-Stakes Domains
M43 Regulation, Geopolitics & Ethics Ch 447–450
  1. Emerging Regulation and Horizon Scanning
  2. Geopolitics of AI
  3. AI Ethics Beyond Compliance
  4. Measuring Governance Effectiveness
M44 Case Studies & Future of Work Ch 451–455
  1. Case Studies: Governance Successes
  2. Case Studies: Governance Failures
  3. AI and the Future of Work
  4. Sustainable and Responsible AI at Scale
  5. AI Assurance as a Discipline
M45 Risk Economics, Assurance & Insurance Ch 456–460
  1. Quantifying and Pricing AI Risk
  2. AI Insurance and Risk Transfer
  3. Governing AI Research and Development
  4. International Coordination and Standards
  5. The Economics of AI Governance
M46 Board, Trust & Program Building Ch 461–470
  1. Advanced Board Governance of AI
  2. AI Governance and Digital Trust
  3. Building an Industry-Leading Program
  4. Governance for AI Platforms and Marketplaces
  5. The Long-Term Trajectory of AI Governance
  6. Synthesis: The Complete Practitioner
  7. Designing a Greenfield Governance Program
  8. Transforming a Failing Program
  9. The Governance Leader as Strategist
  10. Mastery Capstone - The Integrated Program
M47 Mastery - Advanced Track Ch 471–500
  1. Governing Generative AI - Advanced
  2. Governing Foundation Models - Advanced
  3. Agentic AI and Autonomous Systems - Advanced
  4. Frontier Models and Systemic Risk - Advanced
  5. Multi-Agent and Compound Systems - Advanced
  6. AI Governance in High-Stakes Domains - Advanced
  7. Emerging Regulation and Horizon Scanning - Advanced
  8. Geopolitics of AI - Advanced
  9. AI Ethics Beyond Compliance - Advanced
  10. Measuring Governance Effectiveness - Advanced
  11. Case Studies: Governance Successes - Advanced
  12. Case Studies: Governance Failures - Advanced
  13. AI and the Future of Work - Advanced
  14. Sustainable and Responsible AI at Scale - Advanced
  15. AI Assurance as a Discipline - Advanced
  16. Quantifying and Pricing AI Risk - Advanced
  17. AI Insurance and Risk Transfer - Advanced
  18. Governing AI Research and Development - Advanced
  19. International Coordination and Standards - Advanced
  20. The Economics of AI Governance - Advanced
  21. Advanced Board Governance of AI - Advanced
  22. AI Governance and Digital Trust - Advanced
  23. Building an Industry-Leading Program - Advanced
  24. Governance for AI Platforms and Marketplaces - Advanced
  25. The Long-Term Trajectory of AI Governance - Advanced
  26. Synthesis: The Complete Practitioner - Advanced
  27. Designing a Greenfield Governance Program - Advanced
  28. Transforming a Failing Program - Advanced
  29. The Governance Leader as Strategist - Advanced
  30. Mastery Capstone - The Integrated Program - Advanced
M48 Mastery - Applied Track Ch 501–530
  1. Governing Generative AI - Applied
  2. Governing Foundation Models - Applied
  3. Agentic AI and Autonomous Systems - Applied
  4. Frontier Models and Systemic Risk - Applied
  5. Multi-Agent and Compound Systems - Applied
  6. AI Governance in High-Stakes Domains - Applied
  7. Emerging Regulation and Horizon Scanning - Applied
  8. Geopolitics of AI - Applied
  9. AI Ethics Beyond Compliance - Applied
  10. Measuring Governance Effectiveness - Applied
  11. Case Studies: Governance Successes - Applied
  12. Case Studies: Governance Failures - Applied
  13. AI and the Future of Work - Applied
  14. Sustainable and Responsible AI at Scale - Applied
  15. AI Assurance as a Discipline - Applied
  16. Quantifying and Pricing AI Risk - Applied
  17. AI Insurance and Risk Transfer - Applied
  18. Governing AI Research and Development - Applied
  19. International Coordination and Standards - Applied
  20. The Economics of AI Governance - Applied
  21. Advanced Board Governance of AI - Applied
  22. AI Governance and Digital Trust - Applied
  23. Building an Industry-Leading Program - Applied
  24. Governance for AI Platforms and Marketplaces - Applied
  25. The Long-Term Trajectory of AI Governance - Applied
  26. Synthesis: The Complete Practitioner - Applied
  27. Designing a Greenfield Governance Program - Applied
  28. Transforming a Failing Program - Applied
  29. The Governance Leader as Strategist - Applied
  30. Mastery Capstone - The Integrated Program - Applied
M49 Mastery - Deep Dive Track Ch 531–559
  1. Governing Generative AI - Deep Dive
  2. Governing Foundation Models - Deep Dive
  3. Agentic AI and Autonomous Systems - Deep Dive
  4. Frontier Models and Systemic Risk - Deep Dive
  5. Multi-Agent and Compound Systems - Deep Dive
  6. AI Governance in High-Stakes Domains - Deep Dive
  7. Emerging Regulation and Horizon Scanning - Deep Dive
  8. Geopolitics of AI - Deep Dive
  9. AI Ethics Beyond Compliance - Deep Dive
  10. Measuring Governance Effectiveness - Deep Dive
  11. Case Studies: Governance Successes - Deep Dive
  12. Case Studies: Governance Failures - Deep Dive
  13. AI and the Future of Work - Deep Dive
  14. Sustainable and Responsible AI at Scale - Deep Dive
  15. AI Assurance as a Discipline - Deep Dive
  16. Quantifying and Pricing AI Risk - Deep Dive
  17. AI Insurance and Risk Transfer - Deep Dive
  18. Governing AI Research and Development - Deep Dive
  19. International Coordination and Standards - Deep Dive
  20. The Economics of AI Governance - Deep Dive
  21. Advanced Board Governance of AI - Deep Dive
  22. AI Governance and Digital Trust - Deep Dive
  23. Building an Industry-Leading Program - Deep Dive
  24. Governance for AI Platforms and Marketplaces - Deep Dive
  25. The Long-Term Trajectory of AI Governance - Deep Dive
  26. Synthesis: The Complete Practitioner - Deep Dive
  27. Designing a Greenfield Governance Program - Deep Dive
  28. Transforming a Failing Program - Deep Dive
  29. The Governance Leader as Strategist - Deep Dive
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Formats

How you learn

  • Self-paced with lifetime access
  • Instructor-led cohorts
  • Private corporate and enterprise delivery
  • Certificate and digital badge
FAQ

AI Governance & Responsible AI Course - answered

What is the AI Governance & Responsible AI Course?

A complete, self-paced certification program that builds the AI governance practitioner across 559 chapters in six phases - literacy, the rulebook, the controls, operations, leadership, and mastery.

Who is it for?

AI governance leads, chief-data and AI-risk officers, model-risk and compliance leaders, and practitioners building the governance function - from newcomers through to enterprise leaders.

Do I need a technical background?

No. It builds just enough technical fluency to ask sharp questions, and focuses on governance, risk, law, operations, and leadership rather than model implementation.

Which regulations and frameworks does it cover?

The EU AI Act, NIST AI RMF, ISO 42001, and sector-specific rules, taught with a crosswalk technique that maps one internal standard to every applicable regime.

How is it structured?

Six phases with continuous, global chapter numbering from 1 to 559. Each phase has its own labs and a capstone, and the phases build from literacy to mastery.

Is it hands-on?

Yes. Chapters pair concepts with labs - for example, classifying statements as governance, ethics, or compliance, routing use cases through the operating model, and building an AI inventory - and phase capstones produce board-ready deliverables.

Is there a certificate?

Yes. It is a certification program, awarding a Durga Analytics certificate and digital badge.

Is there a downloadable brochure?

Yes, from the download button at the top of this page.

AI Governance & Responsible AI Course for you or your team

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