AI Governance, Risk & Cybersecurity

This certification provides a practical, internationally adaptable system for governing generative AI and agentic AI. It treats AI as a socio-technical system: model capability matters, but the real-world risk of an AI deployment comes from its purpose, data, users, affected people, connected tools, authority, autonomy, suppliers, operating environment, and controls.

The approach is a global control baseline plus local overlays — apply consistent governance principles everywhere, then layer in the legal, sectoral, contractual, and cultural requirements of each jurisdiction you operate in. This course is not legal advice; organizations should obtain qualified jurisdiction-specific advice before using AI in a regulated, high-impact, cross-border, or rights-affecting context.

By the end of this course you will be able to:

• Classify AI systems by purpose, function, autonomy, authority, data sensitivity, impact, and reach
• Establish an AI governance operating model with strategy, policy, accountability, risk management, control evidence, and continual improvement
• Develop use-case charters, system cards, AI inventories, impact assessments, data-flow maps, risk registers, vendor assessments, and incident evidence
• Govern generative AI risks such as confabulation, content integrity, privacy exposure, and harmful or misleading output
• Govern agentic AI risks such as tool misuse, excessive authority, prompt injection, retrieval poisoning, memory poisoning, identity abuse, and multi-agent trust failures
• Apply transparency, explainability, fairness, privacy, security, human oversight, and auditability appropriate to the use case
• Build and test monitoring, incident response, emergency-stop, and continuous-assurance processes

Fifteen modules take you from first principles through to operational control, organized in four parts: foundations, lifecycle and accountability, risk and data governance, and operational security — closing with an assurance and improvement module.

• Module 1: AI Taxonomy and the Governance Challenge
• Module 2: Introduction to AI Governance and Agentic AI
• Module 3: AI Governance Architecture, Strategy, Policy, and Acceptable Use
• Module 4: Use Case Development and Business Alignment
• Module 5: System Cards, Model Cards, and Control Documentation
• Module 6: Lifecycle Onboarding, Inventory, Registration, Ownership, and Retirement
• Module 7: RACI Development, Oversight, and Accountability
• Module 8: Regulatory Compliance, Risk Management, Impact Assessment, and Corporate Responsibility
• Module 9: Data Privacy, Protection, Provenance, and Sovereignty
• Module 10: Third-Party Governance and AI Vendor Risk
• Module 11: Access Management and Autonomy Management
• Module 12: Transparency, Explainability, Traceability, and Auditability
• Module 13: Bias, Fairness, and Responsible Decisioning
• Module 14: Agentic and Generative AI Security Engineering
• Module 15: Emerging Threats, Monitoring, Incident Response, Continuous Assurance, and Improvement

Every module pairs its concepts with a practical deliverable — a worksheet, charter, RACI matrix, inventory schema, or runbook you can adapt directly into your own governance program. The course also includes an international regulatory overlay guide, sector-specific implementation tracks, an implementation toolkit, and a practical capstone scenario, and closes with a 55-question proctored-style final examination with full answer rationales.