AI Governance & Responsible AI
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.
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.
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.
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.
6 phases · 559 chapters
Numbering is global and continuous across the whole program. Each phase builds on the last, from literacy to mastery. Expand any phase for its scope and representative chapters.
P1 Literacy 25 chapters
- What Is AI Governance
- Why AI Governance Matters in 2025+
- Enterprise Risks from AI
- Key Roles & Stakeholders
- The Governance Operating Model
- Building an AI Inventory
- The Global AI Regulatory Landscape
- The EU AI Act in Depth
- The NIST AI Risk Management Framework
- ISO/IEC 42001
- Sector-Specific AI Regulation
- Data Protection and AI
- Automated Decision-Making Rights
- Transparency and Disclosure Obligations
- AI Liability and Accountability Regimes
- The UK Approach to AI Regulation
- The US Federal and State Patchwork
- Asia-Pacific Regulatory Approaches
- Comparing Global Regulatory Regimes
- Trust, Transparency, and the Social License
- Introduction to AI Risk Appetite
- The Governance Leader's Toolkit Overview
- Reading and Interpreting AI Regulation
- Case Studies in AI Governance Foundations
- Phase 1 Capstone - The Literacy Brief
P2 The Rulebook 55 chapters
- The Global AI Regulatory Landscape
- The EU AI Act in Depth
- The NIST AI Risk Management Framework
- ISO/IEC 42001
- Sector-Specific AI Regulation: Finance
- Sector-Specific AI Regulation: Healthcare
- Sector-Specific AI Regulation: Employment & HR
- Data Protection and AI: The GDPR Intersection
- Automated Decision-Making Rights
- Transparency and Disclosure Obligations
- AI Liability and Accountability Regimes
- The UK Approach to AI Regulation
- The US Federal and State Patchwork
- Asia-Pacific AI Frameworks
- Building a Regulatory Crosswalk
- Intellectual Property and AI
- AI and Competition Law
- Consumer Protection and AI
- Sector Deep Dive: Insurance
- Sector Deep Dive: Public Sector & Government
- Sector Deep Dive: Critical Infrastructure
- Biometrics and Facial Recognition Law
- Children and Vulnerable Groups
- AI in Marketing and Advertising
- Content Moderation and Platform Rules
- Open Source and AI Governance
- Foundation Models and General-Purpose AI Rules
- Cross-Border Data Flows and AI
- Standards Bodies and the Standardization Landscape
- Certification, Audit, and Conformity Ecosystems
- Voluntary Codes and Industry Commitments
- Regulatory Sandboxes and Innovation Pathways
- Enforcement, Penalties, and Regulatory Actions
- Horizon Scanning for New Regulation
- Negotiating AI Clauses in Contracts
- Regulatory Relationships and Engagement
- AI Governance in Mergers and Acquisitions
- Records, Documentation, and Evidentiary Duties
- The Politics and Economics of AI Regulation
- Comparative Regulatory Philosophies
- AI and Sector Convergence Risks
- Emerging Liability for Autonomous Systems
- Whistleblowing and Internal Reporting Duties
- Regulatory Reporting and Notifications
- AI Governance and Financial Disclosure
- National Security and Export Controls
- Accessibility and Inclusive-Design Duties
- Environmental and Sustainability Obligations
- Litigation and Case Law Trends
- Regulatory Interpretation and Legal Opinions
- Multi-Jurisdiction Program Design
- AI Governance Frameworks Compared
- The Rulebook in Practice: An Integrated Case
- Future Regulatory Directions
- Phase 2 Capstone - The Regulatory Strategy
P3 The Controls 120 chapters
- AI Risk Assessment Fundamentals
- Building a Risk Taxonomy
- Risk Scoring and Tiering Methods
- Impact Assessments for AI
- Bias and Fairness: Concepts
- Fairness Metrics in Practice
- Bias Testing and Auditing
- Bias Mitigation Techniques
- Explainability: Concepts and Stakes
- Explainability Techniques for Leaders
- Transparency by Design
- Privacy-Preserving AI Techniques
- Data Governance Controls for AI
- Security of AI Systems: Threat Landscape
- Adversarial Robustness Controls
- Securing Generative and LLM Systems
- Human Oversight Design
- Human-in-the-Loop Patterns
- Model Validation and Testing
- Documentation and Model Cards
- Monitoring AI in Production
- Drift Detection and Response
- Incident Detection and Classification
- Third-Party and Vendor Risk Controls
- Supply-Chain and Foundation-Model Risk
- Control Selection and Design
- Control Testing and Assurance
- Residual Risk and Acceptance
- Red-Teaming AI Systems
- Safety Engineering for AI
- AI Risk Assessment Fundamentals - Advanced
- Building a Risk Taxonomy - Advanced
- Risk Scoring and Tiering Methods - Advanced
- Impact Assessments for AI - Advanced
- Bias and Fairness: Concepts - Advanced
- Fairness Metrics in Practice - Advanced
- Bias Testing and Auditing - Advanced
- Bias Mitigation Techniques - Advanced
- Explainability: Concepts and Stakes - Advanced
- Explainability Techniques for Leaders - Advanced
- Transparency by Design - Advanced
- Privacy-Preserving AI Techniques - Advanced
- Data Governance Controls for AI - Advanced
- Security of AI Systems: Threat Landscape - Advanced
- Adversarial Robustness Controls - Advanced
- Securing Generative and LLM Systems - Advanced
- Human Oversight Design - Advanced
- Human-in-the-Loop Patterns - Advanced
- Model Validation and Testing - Advanced
- Documentation and Model Cards - Advanced
- Monitoring AI in Production - Advanced
- Drift Detection and Response - Advanced
- Incident Detection and Classification - Advanced
- Third-Party and Vendor Risk Controls - Advanced
- Supply-Chain and Foundation-Model Risk - Advanced
- Control Selection and Design - Advanced
- Control Testing and Assurance - Advanced
- Residual Risk and Acceptance - Advanced
- Red-Teaming AI Systems - Advanced
- Safety Engineering for AI - Advanced
- AI Risk Assessment Fundamentals - Applied
- Building a Risk Taxonomy - Applied
- Risk Scoring and Tiering Methods - Applied
- Impact Assessments for AI - Applied
- Bias and Fairness: Concepts - Applied
- Fairness Metrics in Practice - Applied
- Bias Testing and Auditing - Applied
- Bias Mitigation Techniques - Applied
- Explainability: Concepts and Stakes - Applied
- Explainability Techniques for Leaders - Applied
- Transparency by Design - Applied
- Privacy-Preserving AI Techniques - Applied
- Data Governance Controls for AI - Applied
- Security of AI Systems: Threat Landscape - Applied
- Adversarial Robustness Controls - Applied
- Securing Generative and LLM Systems - Applied
- Human Oversight Design - Applied
- Human-in-the-Loop Patterns - Applied
- Model Validation and Testing - Applied
- Documentation and Model Cards - Applied
- Monitoring AI in Production - Applied
- Drift Detection and Response - Applied
- Incident Detection and Classification - Applied
- Third-Party and Vendor Risk Controls - Applied
- Supply-Chain and Foundation-Model Risk - Applied
- Control Selection and Design - Applied
- Control Testing and Assurance - Applied
- Residual Risk and Acceptance - Applied
- Red-Teaming AI Systems - Applied
- Safety Engineering for AI - Applied
- AI Risk Assessment Fundamentals - Deep Dive
- Building a Risk Taxonomy - Deep Dive
- Risk Scoring and Tiering Methods - Deep Dive
- Impact Assessments for AI - Deep Dive
- Bias and Fairness: Concepts - Deep Dive
- Fairness Metrics in Practice - Deep Dive
- Bias Testing and Auditing - Deep Dive
- Bias Mitigation Techniques - Deep Dive
- Explainability: Concepts and Stakes - Deep Dive
- Explainability Techniques for Leaders - Deep Dive
- Transparency by Design - Deep Dive
- Privacy-Preserving AI Techniques - Deep Dive
- Data Governance Controls for AI - Deep Dive
- Security of AI Systems: Threat Landscape - Deep Dive
- Adversarial Robustness Controls - Deep Dive
- Securing Generative and LLM Systems - Deep Dive
- Human Oversight Design - Deep Dive
- Human-in-the-Loop Patterns - Deep Dive
- Model Validation and Testing - Deep Dive
- Documentation and Model Cards - Deep Dive
- Monitoring AI in Production - Deep Dive
- Drift Detection and Response - Deep Dive
- Incident Detection and Classification - Deep Dive
- Third-Party and Vendor Risk Controls - Deep Dive
- Supply-Chain and Foundation-Model Risk - Deep Dive
- Control Selection and Design - Deep Dive
- Control Testing and Assurance - Deep Dive
- Residual Risk and Acceptance - Deep Dive
- Red-Teaming AI Systems - Deep Dive
- Safety Engineering for AI - Deep Dive
P4 Operations 130 chapters
- The AI Use-Case Lifecycle in Operation
- Designing the Intake Process
- Running an AI Governance Committee
- Decision Rights and Escalation in Practice
- Governance Tooling and the GRC Stack
- Metrics, KPIs, and Dashboards
- Board and Executive Reporting
- Audit Readiness and Evidence Management
- Policy and Standard Authoring
- Training and Culture Change
- AI Incident Management End to End
- Post-Incident Review and Learning
- Scaling Governance Across the Enterprise
- Embedding Governance in the SDLC
- Governance for Procurement and Vendors
- Change Management for AI Systems
- The Governance Operating Rhythm
- Cross-Functional Coordination
- Self-Service and Delegated Governance
- Governance Workflow Automation
- Managing the Risk Register
- Continuous Compliance Monitoring
- Governance for Rapid Experimentation
- Stakeholder Communication and Engagement
- Resourcing and Capacity Planning
- Governance Documentation Systems
- Operationalizing Human Oversight
- Managing Model Inventories at Scale
- Service-Level and Performance Management
- Operations Capstone - Running the Function
- The AI Use-Case Lifecycle in Operation - Advanced
- Designing the Intake Process - Advanced
- Running an AI Governance Committee - Advanced
- Decision Rights and Escalation in Practice - Advanced
- Governance Tooling and the GRC Stack - Advanced
- Metrics, KPIs, and Dashboards - Advanced
- Board and Executive Reporting - Advanced
- Audit Readiness and Evidence Management - Advanced
- Policy and Standard Authoring - Advanced
- Training and Culture Change - Advanced
- AI Incident Management End to End - Advanced
- Post-Incident Review and Learning - Advanced
- Scaling Governance Across the Enterprise - Advanced
- Embedding Governance in the SDLC - Advanced
- Governance for Procurement and Vendors - Advanced
- Change Management for AI Systems - Advanced
- The Governance Operating Rhythm - Advanced
- Cross-Functional Coordination - Advanced
- Self-Service and Delegated Governance - Advanced
- Governance Workflow Automation - Advanced
- Managing the Risk Register - Advanced
- Continuous Compliance Monitoring - Advanced
- Governance for Rapid Experimentation - Advanced
- Stakeholder Communication and Engagement - Advanced
- Resourcing and Capacity Planning - Advanced
- Governance Documentation Systems - Advanced
- Operationalizing Human Oversight - Advanced
- Managing Model Inventories at Scale - Advanced
- Service-Level and Performance Management - Advanced
- Operations Capstone - Running the Function - Advanced
- The AI Use-Case Lifecycle in Operation - Applied
- Designing the Intake Process - Applied
- Running an AI Governance Committee - Applied
- Decision Rights and Escalation in Practice - Applied
- Governance Tooling and the GRC Stack - Applied
- Metrics, KPIs, and Dashboards - Applied
- Board and Executive Reporting - Applied
- Audit Readiness and Evidence Management - Applied
- Policy and Standard Authoring - Applied
- Training and Culture Change - Applied
- AI Incident Management End to End - Applied
- Post-Incident Review and Learning - Applied
- Scaling Governance Across the Enterprise - Applied
- Embedding Governance in the SDLC - Applied
- Governance for Procurement and Vendors - Applied
- Change Management for AI Systems - Applied
- The Governance Operating Rhythm - Applied
- Cross-Functional Coordination - Applied
- Self-Service and Delegated Governance - Applied
- Governance Workflow Automation - Applied
- Managing the Risk Register - Applied
- Continuous Compliance Monitoring - Applied
- Governance for Rapid Experimentation - Applied
- Stakeholder Communication and Engagement - Applied
- Resourcing and Capacity Planning - Applied
- Governance Documentation Systems - Applied
- Operationalizing Human Oversight - Applied
- Managing Model Inventories at Scale - Applied
- Service-Level and Performance Management - Applied
- Operations Capstone - Running the Function - Applied
- The AI Use-Case Lifecycle in Operation - Deep Dive
- Designing the Intake Process - Deep Dive
- Running an AI Governance Committee - Deep Dive
- Decision Rights and Escalation in Practice - Deep Dive
- Governance Tooling and the GRC Stack - Deep Dive
- Metrics, KPIs, and Dashboards - Deep Dive
- Board and Executive Reporting - Deep Dive
- Audit Readiness and Evidence Management - Deep Dive
- Policy and Standard Authoring - Deep Dive
- Training and Culture Change - Deep Dive
- AI Incident Management End to End - Deep Dive
- Post-Incident Review and Learning - Deep Dive
- Scaling Governance Across the Enterprise - Deep Dive
- Embedding Governance in the SDLC - Deep Dive
- Governance for Procurement and Vendors - Deep Dive
- Change Management for AI Systems - Deep Dive
- The Governance Operating Rhythm - Deep Dive
- Cross-Functional Coordination - Deep Dive
- Self-Service and Delegated Governance - Deep Dive
- Governance Workflow Automation - Deep Dive
- Managing the Risk Register - Deep Dive
- Continuous Compliance Monitoring - Deep Dive
- Governance for Rapid Experimentation - Deep Dive
- Stakeholder Communication and Engagement - Deep Dive
- Resourcing and Capacity Planning - Deep Dive
- Governance Documentation Systems - Deep Dive
- Operationalizing Human Oversight - Deep Dive
- Managing Model Inventories at Scale - Deep Dive
- Service-Level and Performance Management - Deep Dive
- Operations Capstone - Running the Function - Deep Dive
- The AI Use-Case Lifecycle in Operation - In Practice
- Designing the Intake Process - In Practice
- Running an AI Governance Committee - In Practice
- Decision Rights and Escalation in Practice - In Practice
- Governance Tooling and the GRC Stack - In Practice
- Metrics, KPIs, and Dashboards - In Practice
- Board and Executive Reporting - In Practice
- Audit Readiness and Evidence Management - In Practice
- Policy and Standard Authoring - In Practice
- Training and Culture Change - In Practice
P5 Leadership 110 chapters
- Building the Governance Team
- Securing Mandate and Sponsorship
- Building the Business Case
- Influencing Without Authority
- Board and C-Suite Communication
- Avoiding the Department of No
- Governance Strategy and Roadmaps
- Maturity Assessment and Improvement
- Vendor and Partner Management
- Crisis Leadership When AI Fails
- Navigating Organizational Resistance
- The Governance Leader's Career Path
- Stakeholder Mapping and Power
- Communicating Risk to Non-Experts
- Building a Governance Culture
- Leading Change at Scale
- Negotiation and Conflict Resolution
- Ethics Leadership and Moral Courage
- Strategic Alignment with the Business
- Measuring Leadership Effectiveness
- Talent, Skills, and Development
- Governance and Innovation Partnership
- Budgeting and Resource Advocacy
- Executive Decision Support
- Reputation and Trust Leadership
- Leading Distributed and Global Teams
- Personal Effectiveness and Resilience
- Mentoring and Building the Profession
- Governance in the Executive Team
- Leadership Capstone - The Governance Leader
- Building the Governance Team - Advanced
- Securing Mandate and Sponsorship - Advanced
- Building the Business Case - Advanced
- Influencing Without Authority - Advanced
- Board and C-Suite Communication - Advanced
- Avoiding the Department of No - Advanced
- Governance Strategy and Roadmaps - Advanced
- Maturity Assessment and Improvement - Advanced
- Vendor and Partner Management - Advanced
- Crisis Leadership When AI Fails - Advanced
- Navigating Organizational Resistance - Advanced
- The Governance Leader's Career Path - Advanced
- Stakeholder Mapping and Power - Advanced
- Communicating Risk to Non-Experts - Advanced
- Building a Governance Culture - Advanced
- Leading Change at Scale - Advanced
- Negotiation and Conflict Resolution - Advanced
- Ethics Leadership and Moral Courage - Advanced
- Strategic Alignment with the Business - Advanced
- Measuring Leadership Effectiveness - Advanced
- Talent, Skills, and Development - Advanced
- Governance and Innovation Partnership - Advanced
- Budgeting and Resource Advocacy - Advanced
- Executive Decision Support - Advanced
- Reputation and Trust Leadership - Advanced
- Leading Distributed and Global Teams - Advanced
- Personal Effectiveness and Resilience - Advanced
- Mentoring and Building the Profession - Advanced
- Governance in the Executive Team - Advanced
- Leadership Capstone - The Governance Leader - Advanced
- Building the Governance Team - Applied
- Securing Mandate and Sponsorship - Applied
- Building the Business Case - Applied
- Influencing Without Authority - Applied
- Board and C-Suite Communication - Applied
- Avoiding the Department of No - Applied
- Governance Strategy and Roadmaps - Applied
- Maturity Assessment and Improvement - Applied
- Vendor and Partner Management - Applied
- Crisis Leadership When AI Fails - Applied
- Navigating Organizational Resistance - Applied
- The Governance Leader's Career Path - Applied
- Stakeholder Mapping and Power - Applied
- Communicating Risk to Non-Experts - Applied
- Building a Governance Culture - Applied
- Leading Change at Scale - Applied
- Negotiation and Conflict Resolution - Applied
- Ethics Leadership and Moral Courage - Applied
- Strategic Alignment with the Business - Applied
- Measuring Leadership Effectiveness - Applied
- Talent, Skills, and Development - Applied
- Governance and Innovation Partnership - Applied
- Budgeting and Resource Advocacy - Applied
- Executive Decision Support - Applied
- Reputation and Trust Leadership - Applied
- Leading Distributed and Global Teams - Applied
- Personal Effectiveness and Resilience - Applied
- Mentoring and Building the Profession - Applied
- Governance in the Executive Team - Applied
- Leadership Capstone - The Governance Leader - Applied
- Building the Governance Team - Deep Dive
- Securing Mandate and Sponsorship - Deep Dive
- Building the Business Case - Deep Dive
- Influencing Without Authority - Deep Dive
- Board and C-Suite Communication - Deep Dive
- Avoiding the Department of No - Deep Dive
- Governance Strategy and Roadmaps - Deep Dive
- Maturity Assessment and Improvement - Deep Dive
- Vendor and Partner Management - Deep Dive
- Crisis Leadership When AI Fails - Deep Dive
- Navigating Organizational Resistance - Deep Dive
- The Governance Leader's Career Path - Deep Dive
- Stakeholder Mapping and Power - Deep Dive
- Communicating Risk to Non-Experts - Deep Dive
- Building a Governance Culture - Deep Dive
- Leading Change at Scale - Deep Dive
- Negotiation and Conflict Resolution - Deep Dive
- Ethics Leadership and Moral Courage - Deep Dive
- Strategic Alignment with the Business - Deep Dive
- Measuring Leadership Effectiveness - Deep Dive
P6 Mastery 119 chapters
- Governing Generative AI
- Governing Foundation Models
- Agentic AI and Autonomous Systems
- Frontier Models and Systemic Risk
- Multi-Agent and Compound Systems
- AI Governance in High-Stakes Domains
- Emerging Regulation and Horizon Scanning
- Geopolitics of AI
- AI Ethics Beyond Compliance
- Measuring Governance Effectiveness
- Case Studies: Governance Successes
- Case Studies: Governance Failures
- AI and the Future of Work
- Sustainable and Responsible AI at Scale
- AI Assurance as a Discipline
- Quantifying and Pricing AI Risk
- AI Insurance and Risk Transfer
- Governing AI Research and Development
- International Coordination and Standards
- The Economics of AI Governance
- Advanced Board Governance of AI
- AI Governance and Digital Trust
- Building an Industry-Leading Program
- Governance for AI Platforms and Marketplaces
- The Long-Term Trajectory of AI Governance
- Synthesis: The Complete Practitioner
- Designing a Greenfield Governance Program
- Transforming a Failing Program
- The Governance Leader as Strategist
- Mastery Capstone - The Integrated Program
- Governing Generative AI - Advanced
- Governing Foundation Models - Advanced
- Agentic AI and Autonomous Systems - Advanced
- Frontier Models and Systemic Risk - Advanced
- Multi-Agent and Compound Systems - Advanced
- AI Governance in High-Stakes Domains - Advanced
- Emerging Regulation and Horizon Scanning - Advanced
- Geopolitics of AI - Advanced
- AI Ethics Beyond Compliance - Advanced
- Measuring Governance Effectiveness - Advanced
- Case Studies: Governance Successes - Advanced
- Case Studies: Governance Failures - Advanced
- AI and the Future of Work - Advanced
- Sustainable and Responsible AI at Scale - Advanced
- AI Assurance as a Discipline - Advanced
- Quantifying and Pricing AI Risk - Advanced
- AI Insurance and Risk Transfer - Advanced
- Governing AI Research and Development - Advanced
- International Coordination and Standards - Advanced
- The Economics of AI Governance - Advanced
- Advanced Board Governance of AI - Advanced
- AI Governance and Digital Trust - Advanced
- Building an Industry-Leading Program - Advanced
- Governance for AI Platforms and Marketplaces - Advanced
- The Long-Term Trajectory of AI Governance - Advanced
- Synthesis: The Complete Practitioner - Advanced
- Designing a Greenfield Governance Program - Advanced
- Transforming a Failing Program - Advanced
- The Governance Leader as Strategist - Advanced
- Mastery Capstone - The Integrated Program - Advanced
- Governing Generative AI - Applied
- Governing Foundation Models - Applied
- Agentic AI and Autonomous Systems - Applied
- Frontier Models and Systemic Risk - Applied
- Multi-Agent and Compound Systems - Applied
- AI Governance in High-Stakes Domains - Applied
- Emerging Regulation and Horizon Scanning - Applied
- Geopolitics of AI - Applied
- AI Ethics Beyond Compliance - Applied
- Measuring Governance Effectiveness - Applied
- Case Studies: Governance Successes - Applied
- Case Studies: Governance Failures - Applied
- AI and the Future of Work - Applied
- Sustainable and Responsible AI at Scale - Applied
- AI Assurance as a Discipline - Applied
- Quantifying and Pricing AI Risk - Applied
- AI Insurance and Risk Transfer - Applied
- Governing AI Research and Development - Applied
- International Coordination and Standards - Applied
- The Economics of AI Governance - Applied
- Advanced Board Governance of AI - Applied
- AI Governance and Digital Trust - Applied
- Building an Industry-Leading Program - Applied
- Governance for AI Platforms and Marketplaces - Applied
- The Long-Term Trajectory of AI Governance - Applied
- Synthesis: The Complete Practitioner - Applied
- Designing a Greenfield Governance Program - Applied
- Transforming a Failing Program - Applied
- The Governance Leader as Strategist - Applied
- Mastery Capstone - The Integrated Program - Applied
- Governing Generative AI - Deep Dive
- Governing Foundation Models - Deep Dive
- Agentic AI and Autonomous Systems - Deep Dive
- Frontier Models and Systemic Risk - Deep Dive
- Multi-Agent and Compound Systems - Deep Dive
- AI Governance in High-Stakes Domains - Deep Dive
- Emerging Regulation and Horizon Scanning - Deep Dive
- Geopolitics of AI - Deep Dive
- AI Ethics Beyond Compliance - Deep Dive
- Measuring Governance Effectiveness - Deep Dive
- Case Studies: Governance Successes - Deep Dive
- Case Studies: Governance Failures - Deep Dive
- AI and the Future of Work - Deep Dive
- Sustainable and Responsible AI at Scale - Deep Dive
- AI Assurance as a Discipline - Deep Dive
- Quantifying and Pricing AI Risk - Deep Dive
- AI Insurance and Risk Transfer - Deep Dive
- Governing AI Research and Development - Deep Dive
- International Coordination and Standards - Deep Dive
- The Economics of AI Governance - Deep Dive
- Advanced Board Governance of AI - Deep Dive
- AI Governance and Digital Trust - Deep Dive
- Building an Industry-Leading Program - Deep Dive
- Governance for AI Platforms and Marketplaces - Deep Dive
- The Long-Term Trajectory of AI Governance - Deep Dive
- Synthesis: The Complete Practitioner - Deep Dive
- Designing a Greenfield Governance Program - Deep Dive
- Transforming a Failing Program - Deep Dive
- The Governance Leader as Strategist - Deep Dive
How you learn
- Self-paced with lifetime access
- Mentor-led cohorts
- Private corporate and enterprise delivery
- Certificate and digital badge
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
Enquire about enrolment, or scope a private corporate cohort.