AAISM: ISACA Advanced in AI Security Management

Module 1: AI Governance and Program Management

A. Stakeholder Considerations, Industry Frameworks, and Regulatory Requirements

• Organizational Structure and Overall Governance
• Roles and Responsibilities
• Charter and Steering Committee
• Identifying Stakeholders
• Risk Appetite and Tolerance
• Frameworks, Standards, and Regulations
• Selecting appropriate Frameworks
• Business and Use Cases for AI
• Privacy Considerations

B. AI-related Strategies, Policies, and Procedures

• AI Strategy
• Consumer v. Enterprise
• Buy vs. Build
• AI Policies
• Responsible Use
• Acceptable Use
• AI Procedures
• Implementation
• Manuals
• Ethics

C. AI Asset and Data Life Cycle Management

• AI Asset and Data Inventory
• Inventory management
• Model cards
• Data handling, classification, discovery
• Data Augmentation and Cleaning
• Data Storage
• Data Protection
• Destruction

D. AI Security Program Development and Management

• Documented Program Plan
• Security team, roles, responsibilities, and proficiencies
• Alignment to existing info sec
• Use of AI-enabled security tools in the program
• Metrics and management
• KRIs and KPIs for AI use with regard to the security
• Management reporting

E. Business Continuity and Incident Response

• Incident detection
• Notification
• Incident classification
• Criticality and severity
• Resiliency
• Business Continuity Plan
• Red-button requirements for compliance
• Incident response playbooks specifically for AI
• Break glass policies/ go no go
• Authority
• RTO RPO – AI perspective
• Disaster recovery
• Testing

Module 2: AI Risk Management

A. AI Risk Assessment, Thresholds, and Treatment

• Impact assessment
• conformity assessment
• PIAs
• Risk documentation
• Acceptable levels of risk
• Treatment plans
• KRIs and KPIs for AI us

B. AI-related Strategies, Policies, and Procedures

• PEN test
• Vulnerability tests
• Red teaming
• AI related vulnerabilities
• Adversarial threats
• Threat intelligence
• AI-enabled threats/Attack chains
• Anomalies
• Threat landscape
• Deep fakes
• Insider threat
• AI agents

C. AI Vendor and Supply Chain Management

• Dependencies of software packages and libraries
• Vendor due diligence and contracts
• SLAs
• Vendor usage
• Accountability models
• Provider vs. deployer
• Third, fourth, and fifth parties
• Ownership and intellectual property
• Access controls
• Liability
• Vendor monitoring for risk and changes

Module 3: AI Technologies and Controls

A. AI Security Architecture and Design

• Change management
• SDL
• Secure by design
• Securing infrastructure as code
• Data flows
• Approved base models
• Interconnectivity and interaction with architecture

B. AI Life Cycle (e.g., model selection, training, and validation)

• Testing models interconnectivity
• Linkages between models
• Regression
• Model testing
• Progression
• TEVV
• Model accuracy testing and evaluation

C. Data Management Controls

• Data collection
• Data control
• Data Poisoning
• BIAS
• Accuracy
• Data position requirements

D. Privacy, Ethical, Trust and Safety Controls

• Explainability
• Privacy controls – like right to be forgotten, data subject rights
• Consent
• Transparency
• Decision making
• Fairness
• Ethics
• Automated decision making
• Human in the loop
• Trust and safety – content moderation
• Potential harm
• Environmental impacts
• Data minimization and anonymization

E. Security Controls and Monitoring

• Security monitoring metrics
• Selecting the right controls
• Implementing controls
• Self-assessment of controls (CSA)
• Control life cycle
• Continuous monitoring
• KPIs and KRIs for security controls and monitoring
• Technical controls
• Threat controls mapping
• Security awareness training