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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q64-Q69):
NEW QUESTION # 64
A data scientist creating categories and training the algorithm on large data sets is an example of which type of AI model learning technique?
Answer: D
Explanation:
AAISM classifies learning paradigms by the presence of labeled targets. Creating categories (labels) and training on them is supervised learning, where input features are mapped to known outputs and optimization minimizes prediction error against ground truth. Unsupervised (B) discovers structure without labels; reinforcement (A) optimizes behavior via rewards; "machine learning" (C) is the broad field, not the specific technique.
References: AI Security Management™ (AAISM) Body of Knowledge - AI/ML Foundations; Learning Paradigms and Data Requirements. AAISM Study Guide - Supervised vs. Unsupervised vs. Reinforcement Learning; Label Quality and Model Performance Dependencies.
NEW QUESTION # 65
Which of the following is the MOST effective strategy for penetration testers assessing the security of an AI model against membership inference attacks?
Answer: B
Explanation:
AAISM identifies confidence-score analysis as a principal technique for evaluating exposure to membership inference: models often yield measurably higher confidence for points seen during training. Testers compare output probabilities/entropies for known in-training vs. out-of-training samples to assess leakage. Disabling logs (A) reduces evidence; test-set accuracy (B) does not measure privacy leakage; synthetic data generation (D) is a mitigation strategy, not a penetration-testing method.
References: AI Security Management (AAISM) Body of Knowledge - Model Privacy Threats:
Membership Inference; Red/Blue Team Evaluation Techniques; Confidence/Entropy-based Privacy Testing.
NEW QUESTION # 66
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?
Answer: A
Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance. Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories as a Prerequisite to Risk Analysis) ISACA AI Security Management - Asset Visibility and Risk Categorization
NEW QUESTION # 67
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?
Answer: B
Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.
NEW QUESTION # 68
Within an incident handling process, which of the following would BEST help restore end-user trust in an AI system?
Answer: A
Explanation:
AAISM highlights that post-incident remediation and demonstrating lessons learned is essential to restoring trust. Governance guidance specifies that stakeholders regain confidence only when organizations show clear corrective actions, transparency, and improvements to prevent recurrence.
Validating outputs (B) supports accuracy but is not trust-restoring. Monitoring (C) and prioritization (D) relate to operations, not trust rebuilding.
References: AAISM Study Guide - AI Governance; Incident Response and Trust Restoration.
NEW QUESTION # 69
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