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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 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.
Topic 3
  • 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q385-Q390):

NEW QUESTION # 385
An attacker triggers several AI agents to execute CPU-heavy tasks simultaneously, exhausting compute quotas. Which of the following agentic AI threats is this an example of?

Answer: C

Explanation:
Unbounded consumption occurs when AI agents are manipulated into excessively consuming computational resources, such as CPU, memory, or API quotas. Triggering multiple CPU- intensive tasks simultaneously to exhaust compute capacity is a direct example of this threat.


NEW QUESTION # 386
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:

Answer: D

Explanation:
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
References:
AAISM Study Guide - AI Governance and Program Management (Ethical AI and Accountability) ISACA AI Security Management - Responsible AI Practices


NEW QUESTION # 387
An organization plans to use an open-source foundational AI model. Which of the following is MOST important for the AI governance committee to consider when approving its use?

Answer: A

Explanation:
AAISM emphasizes that open-source AI models present elevated data leakage risks because internal data may flow into external, uncontrolled repositories or be used for further training. Governance bodies must prioritize the risk of data exposure, model reuse, data retention uncertainty, and uncontrolled model behavior.
While accuracy (B) and support (C) are important operational considerations, they are not the primary governance risk. Employee privacy rights (D) matter but are encompassed within the broader risk of data leakage.
References: AAISM Study Guide - Open-Source AI Governance; Data Leakage and Uncontrolled Model Behavior.


NEW QUESTION # 388
AI model robustness is PRIMARILY a measure of:

Answer: B

Explanation:
AI model robustness primarily refers to the model's ability to maintain reliable and accurate behavior when exposed to adversarial inputs, manipulated data, or unexpected conditions designed to cause incorrect outputs or failures.


NEW QUESTION # 389
Which of the following BEST ensures the integrity of data sets used to train AI models?

Answer: B

Explanation:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing. Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
References:
AAISM Exam Content Outline - AI Risk Management (Data Integrity and Protection) AI Security Management Study Guide - Cryptographic Controls for Dataset Integrity


NEW QUESTION # 390
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