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NEW QUESTION # 115
A retail organization implements an AI-driven recommendation system that utilizes customer purchase history. Which of the following is the BEST way for the organization to ensure privacy and comply with regulatory standards?
Answer: B
Explanation:
According to the AI Security Management (AAISM) study framework, compliance with privacy and regulatory standards must begin with a formalized process of identifying, documenting, and maintaining applicable obligations. The guidance explicitly notes that organizations should maintain a comprehensive register of legal and regulatory requirements to ensure accountability and alignment with privacy laws. This register serves as the foundation for all governance, risk, and control practices surrounding AI systems that handle personal data.
Maintaining such a register ensures that the recommendation system operates under the principles of privacy by design and privacy by default. It allows decision-makers and auditors to trace every AI data processing activity back to relevant compliance obligations, thereby demonstrating adherence to laws such as GDPR, CCPA, or other jurisdictional mandates.
Other measures listed in the options contribute to good practice but do not achieve the same direct compliance outcome. Retraining models improves technical accuracy but does not address legal obligations. Oversight committees are valuable but require the documented register as a baseline to oversee effectively. Indefinite storage of customer data contradicts regulatory requirements, particularly the principle of data minimization and storage limitation.
AAISM Domain Alignment:
This requirement falls under Domain 1 - AI Governance and Program Management, which emphasizes organizational accountability, policy creation, and maintaining compliance documentation as part of a structured governance program.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management AI Security Management Study Guide - Privacy and Regulatory Compliance Controls ISACA AI Governance Guidance - Maintaining Registers of Applicable Legal Requirements
NEW QUESTION # 116
An e-commerce platform has been subjected to false product image submissions and reviews, as well as fake search and purchase histories. Which of the following is the information security team's BEST course of action in this situation?
Answer: C
Explanation:
Verifying data provenance is the best course of action because it helps confirm the origin, authenticity, and integrity of submitted images, reviews, and behavioral data. This directly addresses the risk of malicious or fabricated data contaminating the platform and influencing AI- driven recommendations or analytics.
NEW QUESTION # 117
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?
Answer: C
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).
NEW QUESTION # 118
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?
Answer: A
Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.
NEW QUESTION # 119
During which phase of the AI life cycle are models assessed for security and determined to be free of malicious manipulations?
Answer: C
Explanation:
The verification phase focuses on ensuring the model has been built correctly and is free from tampering or malicious manipulation by checking integrity, security controls, and adherence to design specifications before deployment.
NEW QUESTION # 120
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