ISACA AAISM Exam Dumps - Smart Way To Get Success

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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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ISACA AAISM Questions - AAISM PDF Dumps [2026]

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q209-Q214):

NEW QUESTION # 209
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?

Answer: D

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 # 210
The PRIMARY reason to conduct a privacy impact assessment (PIA) on an AI system is to:

Answer: A

Explanation:
According to AAISM privacy governance guidance, the primary reason for conducting a PIA is to analyze how personal data is collected, processed, shared, and retained by an AI system. This analysis ensures compliance with privacy laws, mitigates risks to individuals, and informs necessary safeguards. Identifying regulations is part of compliance but is secondary to analyzing actual data handling. Building customer confidence is an outcome, not the main purpose. Checking for poisoned data relates to data quality, not privacy assessment. The fundamental purpose of a PIA is therefore to analyze the handling of personal data.
References:
AAISM Study Guide - AI Governance and Program Management (Privacy Impact Assessments) ISACA AI Security Management - Data Handling and Privacy Risk


NEW QUESTION # 211
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?

Answer: B

Explanation:
AAISM states that AI agent security training should focus on the unique risks of agentic systems, which include:
* prompt injection
* memory control and context hijacking
* unsafe tool execution (agents triggering unauthorized actions)
These risks are specific to autonomous or semi-autonomous AI agents.
Bias, fairness (B) and output moderation (C) are important but not the most critical for agent security. API abuse and plug-in risk (D) matter but are secondary.
References: AAISM Study Guide - Agentic AI Security; Prompt Injection and Tool Execution Risks.


NEW QUESTION # 212
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?

Answer: A

Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive.
Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks.
Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.
References:AI Security Management™ (AAISM) Body of Knowledge: Adversarial ML-Evasion vs.
Poisoning; Robustness and Resilience Controls; Adversarial Training.AAISM Study Guide: Model Hardening Techniques; Evaluation of Robust Accuracy; Security Testing with Adversarial Examples.


NEW QUESTION # 213
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?

Answer: B

Explanation:
AAISM highlights the importance of an AI threat-control matrix that:
* maps threats (e.g., poisoning, exfiltration, injection)
* identifies required controls
* defines assurance/testing activities
* ensures consistent coverage across all AI components
Control baselines (A) are useful but not AI-specific. Red teaming (C) is reactive and not comprehensive.
Vendor reports (D) are supplemental, not sufficient.
References: AAISM Study Guide - AI Risk Treatment; Threat-Control Mapping.


NEW QUESTION # 214
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