Module 1: Introduction to AI and its Risk Landscape

• Defining Artificial Intelligence: Concepts, types, and current trends
• The dual nature of AI: Opportunities, benefits, and inherent challenges
• Categorizing AI risks: Technical, ethical, societal, operational, and legal dimensions
• The critical importance of AI Risk Management for organizational resilience
• Overview of the evolving global regulatory landscape for AI (e.g., GDPR, EU AI Act, US initiatives)

Module 2: Foundations of the NIST AI Risk Management Framework (AI RMF)

• Introduction to the NIST AI RMF 1.0: Purpose, structure, and guiding principles
• Deconstructing the four core functions: Govern, Map, Measure, and Manage
• Understanding AI RMF profiles: Tailoring the framework to specific contexts
• Key stakeholders in AI risk management and their roles and responsibilities
• Integrating AI RMF with existing enterprise risk management (ERM) frameworks

Module 3: Govern: Establishing AI Risk Governance and Accountability

• Developing organizational AI ethics policies and principles
• Defining roles, responsibilities, and accountability structures for AI systems
• Establishing an AI risk culture through training, awareness, and communication
• Designing oversight mechanisms, review boards, and audit processes for AI
• Legal and compliance considerations in AI governance: Data privacy, consumer protection, non-discrimination

Module 4: Map: Identifying and Characterizing AI Risks

• Techniques for comprehensive AI risk identification across the AI lifecycle (design, development, deployment, monitoring)
• Understanding AI system capabilities, limitations, and failure modes
• Identifying data privacy, security, and integrity risks in AI pipelines
• Assessing risks related to bias, fairness, transparency, and explainability
• Conducting AI impact assessments (AIAs) and stakeholder analysis

Module 5: Measure: Analyzing and Quantifying AI Risks

• Developing appropriate metrics and indicators for AI risk assessment
• Applying quantitative and qualitative risk analysis techniques to AI systems
• Leveraging tools and technologies for AI risk measurement and monitoring
• Establishing continuous monitoring and auditing processes for AI performance and compliance
• Effective reporting of AI risk status and trends to diverse stakeholdersLesson Plan

Module 6: Manage: Mitigating and Responding to AI Risks

• Designing and implementing AI risk mitigation strategies: technical controls, process improvements, human oversight
• Developing robust incident response and recovery plans for AI failures, misuse, or adversarial attacks
• Establishing continuous feedback loops for iterative risk management and improvement
• Best practices for responsible AI deployment, maintenance, and eventual decommissioning
• Strategies for effectively communicating AI risks and mitigation efforts to internal and external audiences

Module 7: Advanced Topics and Real-World AI RMF Applications

• Sector-specific applications of AI RMF (e.g., healthcare, finance, critical infrastructure)
• Addressing emerging AI risks: Explainable AI (XAI), adversarial robustness, deepfakes, and synthetic media
• Integrating AI RMF with MLOps, DevSecOps, and secure software development lifecycles
• Global perspectives on AI regulation, standards, and international collaboration
• Practical workshops: Applying the NIST AI RMF to complex real-world AI use cases and developing tailored profiles