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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 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 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 (Q155-Q160):

NEW QUESTION # 155
Which of the following data management techniques BEST improves an AI model's performance by enhancing training data quality?

Answer: B


NEW QUESTION # 156
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 # 157
An organization is considering a Software as a Service (SaaS)-based HR system that uses AI to automate resume vetting and pre-screening of candidates. Which of the following controls is MOST important to review?

Answer: C

Explanation:
AAISM states that HR systems performing candidate evaluation must prioritize training data fairness, representativeness, and bias mitigation because biased HR decisions carry regulatory, ethical, and litigation risks.


NEW QUESTION # 158
An AI research team is developing a natural language processing model that relies on several open-source libraries. Which of the following is the team's BEST course of action to ensure the integrity of the software packages used?

Answer: A

Explanation:
AAISM's technical control guidance emphasizes that when using open-source libraries, the best safeguard for integrity is to scan the packages for malware before installation. This ensures that compromised or malicious code does not enter the AI system environment. Maintaining lists aids consistency but not security. Always using the latest versions may introduce unverified vulnerabilities. Retraining models addresses functionality but not software integrity. Therefore, the strongest protective measure is pre-installation malware scanning of open-source packages.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Software Supply Chain Security) AI Security Management Study Guide - Open-Source Package Risk Mitigation


NEW QUESTION # 159
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: A

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 # 160
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