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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Risk Management31%- AI threat and vulnerability management
  • 1. AI vulnerability assessment
  • 2. Threat modeling for AI systems
- AI risk assessment and treatment
  • 1. Risk thresholds and mitigation
  • 2. Risk identification and analysis
- Third-party and supply chain risk management
  • 1. Vendor risk evaluation
  • 2. Supply chain security controls
Topic 2: AI Governance and Program Management31%- AI policies and procedures
  • 1. Policy implementation and compliance
  • 2. AI-related strategy development
- Stakeholder considerations and governance frameworks
  • 1. AI governance models and oversight
  • 2. Industry frameworks and regulatory requirements
- AI asset and data lifecycle management
  • 1. AI asset inventory and management
  • 2. Data governance
- AI security program management
  • 1. Program development and maintenance
  • 2. Incident response and business continuity
Topic 3: AI Technologies and Controls38%- Trust, safety and compliance controls
  • 1. Compliance and governance validation
  • 2. Responsible AI practices
- AI security architecture and design
  • 1. AI infrastructure protection
  • 2. Secure AI system architecture
- AI security monitoring and operations
  • 1. Security monitoring and logging
  • 2. Operational AI controls
- Data management and privacy controls
  • 1. Privacy and ethical AI controls
  • 2. Data integrity and protection

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
When preparing for an AI incident, which of the following should be done FIRST?

Answer: C

Explanation:
AAISM incident response guidance states the first foundational step is forming a cross-functional AI-aware incident response team, including model developers, data stewards, security leads, and compliance officers. Without the team established, recovery (C), containment (D), or communication channels (A) cannot be effectively designed or executed.


NEW QUESTION # 119
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?

Answer: C

Explanation:
AAISM emphasizes that when introducing innovative AI systems, organizations reduce security and compliance risk by following a phased adoption approach. This allows incremental deployment, controlled testing, and gradual scaling while monitoring risks in real time. Re- evaluating risk appetite and evaluating compliance are important governance steps but do not directly mitigate risks during implementation. Seeking third-party advice can add expertise but does not provide the structured control that phased integration offers. The most effective risk management approach for AI innovation is to adopt a phased rollout strategy.


NEW QUESTION # 120
Which of the following processes involves tagging and labeling raw datasets to create training data sets?

Answer: D

Explanation:
Annotation is the process of tagging, labeling, or marking raw data so it can be used as structured training data for machine learning and AI models. This enables the model to learn patterns, categories, or relationships from the labeled examples.


NEW QUESTION # 121
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

Answer: C

Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
* proof of lawful data disposal
* auditability
* regulatory compliance
* defensibility during inspections
Post-destruction risk assessments (A) are not primary compliance evidence. Backup tests (B) are operational tasks, not decommissioning proof. Policy updates (C) are future improvements.
References: AAISM Study Guide - AI Decommissioning & Regulatory Compliance; Data Destruction Evidence.


NEW QUESTION # 122
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

Answer: B

Explanation:
The most effective strategy to minimize attack surfaces in AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle of least privilege to restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
* Pre-trained models and log centralization are useful but do not directly reduce the attack surface.
* Manual code reviews are important but insufficient against runtime exploitation.
Thus, compartmentalization with least privilege enforcement is the most effective technical safeguard.


NEW QUESTION # 123
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