Course Outline:
ISACA Advanced in AI Security ManagementTM (AAISM) validates security management professionals’ ability to demonstrate their expertise in AI. This credential builds upon existing security best practices to enhance expertise and adapt to the evolving AI-driven landscape, ensuring robust protection and a strategic edge.
Course Duration:
◦ 2 days ◦ Approximately 12 hours
Exam Duration:
◦ 90 questions ◦ Must be completed in 2 hours
Required Prerequisite:
◦ Must possess a CISM or CISSP to be eligible for certification.
CPE Requirements:
• A minimum of 10 hours of CPE/year in the AI domain.
• CPE can be applied to other certifications as well as part of the 20 annual /120 three year requirement.
• No additional three-year requirement.
Course Topics:
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
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
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 Monitorin
• 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
