ISACAISACA Advanced in AI Security Management
The ISACA Advanced in AI Security Management (AAISM) certification is the first and only AI-centric security management credential, designed for experienced security professionals who hold an active CISM or CISSP. It validates your ability to identify, assess, and mitigate AI-specific threats, implement AI security policy, and ensure responsible AI use across the enterprise. Earning AAISM positions you to lead AI security decisions and protect your organization's future.
436 practice questions · Updated 2026-07-30
3Domains
13Objectives
114Concepts
436Questions
AAISM Curriculum
Every domain, objective, and concept the AAISM exam measures.
- Stakeholder Identification
- Stakeholder Engagement Strategies
- Stakeholder Impact Assessment
- Industry Frameworks Overview
- Framework Selection and Application
- Regulatory Landscape
- Regulatory Compliance Mapping
- Integration of Stakeholder, Framework, and Regulatory Considerations
- AI Asset Identification
- AI Asset Classification
- AI Asset Inventory Management
- AI Asset Life Cycle Stages
- Data Life Cycle Management
- Data Governance for AI
- Data Lineage and Provenance
- Data Privacy and Protection
- Data Retention and Disposal
- AI Model Life Cycle Management
- Model Versioning and Reproducibility
- AI Asset Risk Assessment
- AI Asset Compliance and Audit
- AI Asset Monitoring and Maintenance
- AI Asset Retirement and Decommissioning
- Define AI Security Program Scope
- Align AI Security with Governance Frameworks
- Develop AI Security Policies and Standards
- Establish AI Security Roles and Responsibilities
- Integrate AI Security into SDLC
- Manage AI Supply Chain Security
- Implement AI Security Monitoring and Incident Response
- Conduct AI Security Awareness and Training
- Measure and Report AI Security Program Effectiveness
- Continuously Improve AI Security Program
- Business Continuity Planning Fundamentals
- Incident Response Lifecycle
- AI-Specific Incident Types
- AI Incident Response Planning
- Business Impact Analysis for AI
- Recovery Strategies for AI Systems
- Testing and Exercising Plans
- Integration with Organizational Plans
- AI Risk Assessment Process
- AI Risk Identification Techniques
- AI Risk Analysis Methods
- AI Risk Evaluation and Prioritization
- Risk Thresholds Definition
- Risk Threshold Application
- AI Risk Treatment Options
- Risk Treatment Planning
- Residual Risk Assessment
- Risk Treatment Monitoring and Review
- Threat Landscape for AI Systems
- AI Vulnerability Assessment
- Adversarial Machine Learning Attacks
- AI-Specific Attack Vectors
- Threat Modeling for AI
- Vulnerability Management Lifecycle for AI
- AI Risk Scoring and Prioritization
- Monitoring and Detection of AI Threats
- Incident Response for AI Compromise
- Mitigation and Remediation Strategies
- Governance and Compliance in AI Threat Management
- Vendor Risk Assessment
- Supply Chain Mapping
- Contractual Security Requirements
- Vendor Due Diligence
- Continuous Monitoring
- Incident Response Coordination
- Exit Strategy and Data Disposal
- AI Security Architecture Principles
- AI System Threat Modeling
- Secure AI Data Pipeline Design
- AI Model Security Controls
- AI Infrastructure Security
- AI Application Security Integration
- AI Security Architecture Review
- Model Selection Criteria
- Model Training Process
- Model Validation Techniques
- Overfitting and Underfitting
- Bias and Variance Trade-off
- Data Splitting Strategies
- Performance Metrics Interpretation
- Model Governance in Life Cycle
- Data Classification
- Data Lifecycle Management
- Data Quality Management
- Data Provenance and Lineage
- Data Privacy and Protection
- Data Retention and Disposal
- Data Governance Frameworks
- Privacy Controls
- Ethical AI Principles
- Trust in AI Systems
- Safety Controls
- Regulatory Compliance
- Data Governance
- Bias and Fairness
- Explainability and Transparency
- Accountability Mechanisms
- Human Oversight
- Security Control Frameworks
- AI-Specific Threats and Vulnerabilities
- Monitoring AI System Behavior
- Incident Response for AI
- Compliance and Audit
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for AAISM, so none is invented.