ISACA AAISM Exam Cram Questions - New AAISM Test Syllabus

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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 Advanced in AI Security Management (AAISM) Exam Sample Questions (Q223-Q228):

NEW QUESTION # 223
Which of the following methods provides the MOST effective protection against model inversion attacks?

Answer: D

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management™ Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.


NEW QUESTION # 224
Which of the following BEST strengthens information security controls around the use of generative AI applications?

Answer: A

Explanation:
AAISM identifies continuous monitoring of AI outputs-especially generative outputs-as the most effective security control, ensuring that violations, unsafe responses, data leakage, and policy-breaking behavior are detected and corrected.
A kill switch (C) is a last-resort measure, not a primary control. Exceeding benchmarks (A) does not ensure relevance. Validating training data (D) is important but insufficient for generative output risks.
References: AAISM Study Guide - Generative AI Security Controls; Output Monitoring and Policy Alignment.


NEW QUESTION # 225
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

Answer: B

Explanation:
The priority control in AI vendor due diligence is ensuring explicit disclosure of data handling: data flows, purpose limitation, retention/deletion, training vs. inference use, isolation controls, access paths, subcontractors, and storage/transfer boundaries. This disclosure is then tied to contractual commitments and measurable controls. A public policy (Option A) may be incomplete; a pre-procurement external audit (Option C) can be valuable but is not always feasible or targeted to your data use; legal discussions (Option D) are necessary for terms but must be grounded in clear, detailed data-use disclosures to be effective.
References:
AAISM Body of Knowledge: Third-Party AI Risk Management; Data Governance and Usage Controls; Contractual and Technical Safeguards for Vendor AI.
AAISM Study Guide: AI Procurement Due Diligence; Data-Use Transparency (Training vs. Fine-tuning vs.
Inference); Retention, Purpose Limitation, and Cross-Border Controls.


NEW QUESTION # 226
An organization is facing a deepfake attack intended to manipulate stock prices. The organization's crisis communication plan has been activated. Which of the following is MOST important to include in the initial response?

Answer: C

Explanation:
AAISM guidance on crisis management and communication emphasizes that the initial priority in responding to a reputational or market manipulation attack is to provide accurate clarifying information to the public through a pre-approved statement. This ensures stakeholders and markets are given verified facts immediately, limiting the spread of misinformation. While forensic analysis, employee training, and monitoring activities are important, they occur after the immediate need for public trust and damage control is addressed. Pre-approved statements are a central control in AI-related incident response to ensure consistency, timeliness, and credibility in communications.
References:
AAISM Study Guide - AI Governance and Program Management (Incident Response and Crisis Communication) ISACA AI Security Management - Public Communication and Trust Preservation


NEW QUESTION # 227
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 # 228
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