ISACA
Advanced in AI Risk (AAIR)
Manage AI risk across the life cycle.
2 daysAdvanced3 modules
About this course
Manage AI risk across the life cycle. Over two days you will integrate AI risk into governance frameworks, address risk in AI design, data requirements, development and procurement, and run an AI risk programme with scenario assessment and threat modelling, preparing for AAIR.
Curriculum
3 modules
- AI Models, Frameworks, Strategies, and Use Cases
- Types of AI
- AI Frameworks
- Business Use Case and AI Use Case Review
- AI Business Strategies
- AI Organizational Processes and Alignment
- AI Governance Fundamentals
- Alignment to Existing Organizational Structures
- AI Ownership, Oversight, and Accountability
- AI-related Roles and Responsibilities
- Accountability and AI
- RACI for AI Solutions
- AI Policies, Procedures, and Organizational Training
- AI Acceptable Use Policy
- AI Policy Development
- AI Procedures and Manuals
- Organizational 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
- 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 Normalization
- Data Minimization and Privacy Considerations
- 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
Who it's for
Risk professionals holding CRISC or similar credentials.
Before you start
- Requires existing certification (typically one of the following): CRISC (primary path) or similar: CISA, CISM, CGEIT, CDPSE - Designed for experienced risk professionals
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