AI Risk Management Certification

Module 1: Introduction to AI Risk Management

• Understanding AI risk
• Sources of AI risk
• AI lifecycle considerations
• Business impact of AI risks
• Role of risk management

Module 2: AI Risk Landscape

• Operational risks
• Reputational risks
• Security risks
• Privacy risks
• Organizational risks

Module 3: AI Risk Management Principles

• Risk-based thinking
• Accountability
• Transparency
• Human oversight
• Continuous monitoring

Module 4: AI System Lifecycle

• Planning
• Development and acquisition
• Deployment
• Operation and monitoring
• Retirement considerations

Module 5: AI Risk Identification

• Identifying AI use cases
• Mapping potential risks
• Understanding affected stakeholders
• Identifying dependencies
• Documenting risk scenarios

Module 6: AI Risk Assessment

• Likelihood assessment
• Impact assessment
• Risk severity
• Risk prioritization
• Documenting assessment results

Module 7: AI Risk Registers

• Creating risk records
• Risk descriptions
• Risk ownership
• Controls and actions
• Monitoring risk status

Module 8: AI Governance and Accountability

• Governance structures
• Roles and responsibilities
• Decision authority
• Oversight mechanisms
• Accountability across teams

Module 9: Bias and Fairness Risks

• Sources of bias
• Data-related bias
• Model-related bias
• Outcome disparities
• Risk mitigation approaches

Module 10: Data Quality Risks

• Incomplete data
• Inaccurate data
• Unrepresentative data
• Data drift
• Data quality controls

Module 11: Privacy Risks in AI

• Personal information
• Data minimization
• Purpose considerations
• Data access
• Privacy-focused controls

Module 12: AI Security Risks

• Unauthorized access
• Data exposure
• Adversarial risks
• Model and application vulnerabilities
• Security controls

Module 13: Generative AI Risks

• Inaccurate outputs
• Fabricated information
• Sensitive information exposure
• Inappropriate content
• Overreliance on generated outputs

Module 14: Prompt and Input Risks

• Sensitive prompts
• Malicious instructions
• Prompt injection concepts
• Untrusted input
• Input control strategies

Module 15: Output and Decision Risks

• Incorrect outputs
• Misleading recommendations
• Automated decisions
• Human dependency
• Output validation

Module 16: Transparency and Explainability

• Understanding transparency
• Explainability needs
• Communicating AI limitations
• Documenting system behaviour
• Stakeholder understanding

Module 17: Human Oversight

• Human-in-the-loop approaches
• Approval checkpoints
• Escalation mechanisms
• Intervention procedures
• Maintaining meaningful oversight

Module 18: Automation Risk

• Over-automation
• Human dependency
• Loss of oversight
• Automation errors
• Appropriate control points

Module 19: Third-Party AI Risk

• External AI providers
• Vendor assessment
• Data-sharing considerations
• Service dependencies
• Ongoing vendor monitoring

Module 20: AI Supply Chain Risk

• External models
• Data providers
• Integrated services
• Technology dependencies
• Supply chain visibility

Module 21: AI Risk Controls

• Preventive controls
• Detective controls
• Corrective controls
• Manual and automated controls
• Control ownership

Module 22: Designing AI Risk Mitigation Plans

• Selecting mitigation actions
• Assigning responsibilities
• Establishing timelines
• Residual risk
• Tracking implementation

Module 23: Testing AI Controls

• Control testing
• Scenario-based testing
• Reviewing effectiveness
• Identifying control gaps
• Documenting findings

Module 24: AI Risk Monitoring

• Risk indicators
• Performance monitoring
• Control monitoring
• Emerging risks
• Continuous review

Module 25: AI Incident Management

• Identifying incidents
• Incident classification
• Escalation
• Containment and response
• Post-incident review

Module 26: AI Risk Documentation

• System inventories
• Risk assessments
• Control documentation
• Decision records
• Maintaining audit trails

Module 27: AI Risk Reporting

• Management reporting
• Risk dashboards
• Key risk indicators
• Communicating significant risks
• Escalation reporting

Module 28: Building an AI Risk Management Framework

• Defining governance structure
• Establishing risk processes
• Assigning ownership
• Integrating controls
• Continuous improvement

Module 29: Integrating AI Risk with Enterprise Risk

• Enterprise risk management
• Connecting AI and business risks
• Risk ownership
• Organizational reporting
• Cross-functional collaboration

Module 30: AI Risk Culture

• Risk awareness
• Responsible AI behaviours
• Employee responsibilities
• Internal communication
• Continuous learning

Module 31: Practical AI Risk Assessment

• Defining an AI use case
• Identifying risks
• Assessing likelihood and impact
• Selecting controls
• Evaluating residual risk

Module 32: Developing an AI Risk Action Plan

• Reviewing current AI usage
• Identifying priority risks
• Assigning mitigation actions
• Establishing monitoring activities
• Planning continuous improvement