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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models used for fraud detection systems?

Answer: D

Explanation:
AAISM defines differential privacy as a technique that ensures individual data points cannot be reverse- engineered or identified, even when used to train high-risk AI models such as fraud detection systems. Its primary purpose is to protect individual privacy while maintaining aggregate utility.
Accuracy improvements (B) or performance optimizations (A, D) are not core objectives of differential privacy.
References: AAISM Study Guide - Privacy-Preserving Machine Learning; Differential Privacy Fundamentals.


NEW QUESTION # 161
Which of the following approaches BEST helps to reduce model bias?

Answer: A

Explanation:
AAISM frames bias risk primarily as a data problem. The most impactful mitigation is to ensure diversity and representativeness of training data sources, thereby reducing sampling bias and improving fairness across subpopulations. More labels per instance (A) does not correct coverage gaps; reducing update cadence (B) can entrench existing bias; and higher model complexity (C) may overfit or obscure bias without addressing root causes. Diverse, representative datasets-paired with fairness testing-are the recommended first-line control.
References: AI Security Management (AAISM) Body of Knowledge - Bias Identification and Mitigation; Data Quality and Representativeness. AAISM Study Guide - Fairness Risk Controls; Dataset Curation and Coverage Analysis.


NEW QUESTION # 162
During the deployment of a generative AI platform, a risk assessment identified threats related to data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?

Answer: C

Explanation:
Mapping identified AI threats to enterprise control catalogs and integrating AI-specific safeguards ensures that controls are selected based on the organization's existing risk management framework while also addressing unique AI-related risks such as prompt manipulation and data leakage. This provides comprehensive, risk-aligned, and governance-consistent protection.


NEW QUESTION # 163
Which of the following controls BEST builds user trust in AI application output?

Answer: B

Explanation:
Displaying model reasoning helps users understand how conclusions are reached, increasing transparency and confidence in the output, which directly builds trust.


NEW QUESTION # 164
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?

Answer: C

Explanation:
Differential privacy aims to protect the privacy of any single individual's data contribution while still enabling useful aggregate learning and statistical analysis. Noise mechanisms are calibrated so that results remain informative for modeling (e.g., fraud patterns) without revealing whether any particular person's data was included or enabling inference about them. Accuracy, speed, and compute efficiency can be secondary considerations, but the primary objective is privacy protection with utility preserved.
References: AI Security Management™ (AAISM) Body of Knowledge: Privacy-Preserving ML; Differential Privacy Objectives and Mechanisms. AAISM Study Guide: Individual Contribution Protection; Utility- Privacy Trade-offs and Calibration in Applied Models.


NEW QUESTION # 165
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