Real ISACA AAISM Exam Questions with Verified Answers

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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 (Q169-Q174):

NEW QUESTION # 169
Which of the following is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

Answer: C

Explanation:
When enabling genAI capabilities in a critical system, AAISM prioritizes controlling access to the model and its interfaces (prompt surfaces, context windows, tools/functions, and connected data) because exposure expands the attack surface for prompt injection, data exfiltration, jailbreaks, and misuse. Monitoring (C) is necessary but detective; ethics and bias (D) are vital but secondary to immediate safety and security of a mission-critical environment; proposed regulations (B) are not an immediate operational risk.
References: AAISM Body of Knowledge: GenAI Security-Access Governance, Interface Hardening, and Prompt Surface Controls; AAISM Study Guide: Critical System Safeguards-Least Privilege, Guardrails, and Abuse Prevention.


NEW QUESTION # 170
To ensure ethical and responsible AI use, which AI usage policy metric is MOST important to monitor?

Answer: D

Explanation:
AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
* awareness
* practical adoption
* reliance on policy guidance
* safe decision-making behavior
Violations (A) are lagging indicators. Compliance reviews (B) measure oversight, not behavior. Policy review frequency (D) tracks governance updates, not usage.
References: AAISM Study Guide - AI Policy Effectiveness Metrics.


NEW QUESTION # 171
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: B

Explanation:
A confusion matrix explicitly tabulates TP, TN, FP, FN, serving as the basis for derived metrics (precision, recall, F1, specificity) and error analysis. Precision and recall are single metrics derived from the matrix; hyperparameter tuning is a process, not an analysis artifact.
References: AAISM Body of Knowledge: Model Evaluation & Assurance-Classification Metrics and Error Analysis; AAISM Study Guide: Confusion Matrix Fundamentals and Derived Measures.


NEW QUESTION # 172
To ensure the ethical and responsible use of AI, which of the following AI usage policy metrics is MOST important for an organization to monitor?

Answer: C

Explanation:
AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice. Consult frequency (A) and review cadence (D) are activity metrics, not outcomes. Reported violations (B) are lagging indicators and can be deceptive (low numbers may indicate under-reporting).
References:* AI Security Management (AAISM) Body of Knowledge: Program KPIs-policy adoption, stage-gate compliance, audit readiness* AAISM Study Guide: Governance metrics for Responsible AI- coverage of reviews, pass/fail rates, exceptions handling


NEW QUESTION # 173
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?

Answer: D

Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces


NEW QUESTION # 174
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