Advanced in AI Risk (AAIR™) - Self-paced

Course subjects

Domain 1: AI Risk Governance and Framework Integration

AI Models, Frameworks, Strategies, and Use Cases

• Types of AI

• AI Frameworks

• Business Use Case and AI Use Case Review

• AI Business Strategies

AI Organisational Processes and Alignment

• AI Governance Fundamentals

• Alignment to Existing Organisational Structures

AI Ownership, Oversight, and Accountability

• AI-related Roles and Responsibilities

• Accountability and AI

• RACI for AI Solutions

AI Policies, Procedures, and Organisational Training

• AI Acceptable Use Policy

• AI Policy Development

• AI Procedures and Manuals

• Organisational Culture and AI Risk Governance

• Elements of Effective AI Training and Awareness

AI Regulatory Compliance and Legal Considerations

• Compliance With Laws and Regulations

• Gaps in Regulatory Coverage

• Mapping Legal Requirements for AI

• Assessing Legal Exposure and Liability for AI Actions

• Intellectual Property Considerations in AI

• Vendor Contract Review

AI Trustworthiness, Ethical and Societal Implications

• Responsible Use of AI Systems 68

• Bias and Fairness

• Transparency and Explainability

• Trust and Safety

• Human Rights and Societal Impact

• Environmental Impact

Domain 2: AI Life Cycle Risk Management

AI Design, Development, Procurement, and Documentation

• Plan and Design

• Data Requirements for AI Models

• Procurement of AI Solutions

• Build, Adapt, and Document Models

AI Model Training, Testing and Validation

• Sourcing Datasets

• Validating the Data

• Model Training

• Model Testing and Validation

• Model Performance and Fine Tuning

AI Implementation, Maintenance, and Decommissioning

• AI Deployment and Implementation

• Robustness and Scalability Considerations

• Monitoring and Managing Model Drift

• Change Management in AI Systems

• Decommissioning AI Solutions

AI Data and Asset Management

• AI Asset Inventory

• Data Collection for AI

• Data Classification

• Data Confidentiality

• Data Quality

• Data Balancing

• Data Scarcity

• Data Security

• Data Preparation and Normalisation

• Data Minimisation and Privacy Considerations

Domain 3: AI Risk Program Management

AI Risk Scenario Identification and Assessment

• AI Threat Landscape

• AI Threat Modeling

• Development of AI Risk Scenarios

• AI Risk Classification

• AI Risk Assessment

AI Risk Treatment Strategies

• Accept

• Avoid

• Mitigation

• Transfer/Share

AI Controls Management

• AI Control Types and Control Frameworks

• AI Control Selection and Validation

• Control Performance

• Controls Specific to AI Solutions

• Use of AI in Control Management

AI Risk Metrics, Monitoring, and Reporting

• Risk and Performance Metrics

• AI Risk Reportings

AI Supply Chain Risk Management

• AI Vendor Management

• AI Shared Responsibility Model

• AI Software Supply Chain Risk

• Cloud Computing Risk in AI Supply Chains

AI Incident Response, BIA, Business Continuity, and Disaster Recovery

• AI Business Impact Analysis

• Prepare

• Identify and Report

• Assess

• Respond

• Post-incident Review