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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 2
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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Free ISACA AAISM Test Questions - AAISM Reliable Study Materials

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q238-Q243):

NEW QUESTION # 238
Which of the following is the MOST critical success factor for an AI implementation project?

Answer: D

Explanation:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship & Accountability; Strategy-to-Control Alignment for Successful AI Delivery.


NEW QUESTION # 239
Which of the following controls BEST mitigates the inherent limitations of generative AI models?

Answer: A

Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations


NEW QUESTION # 240
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

Answer: D

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 # 241
A preliminary risk assessment of a SaaS-based large language model (LLM) business support system has identified prompt injection, data poisoning, and model exfiltration as material threats. Which of the following is the BEST approach to ensure risks are treated consistently?

Answer: C

Explanation:
AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Treatment Planning; Threat-Control Traceability; Assurance & Evidence Management for AI Systems.


NEW QUESTION # 242
Which of the following is the BEST mitigation technique for a model inversion attack?

Answer: A

Explanation:
Privacy-preserving methods such as differential privacy limit the amount of information a model can reveal about its training data, directly reducing the risk of reconstructing sensitive inputs through model inversion attacks.


NEW QUESTION # 243
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