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NEW QUESTION # 365
Which of the following approaches BEST enables the separation of sensitive and shareable data to prevent an AI chatbot from inadvertently disclosing confidential information?
Answer: C
Explanation:
AAISM materials describe data segregation and segmented access as core technical controls to prevent unintended information disclosure by AI systems. Siloing refers to logically or physically separating data into distinct repositories or contexts, ensuring that sensitive datasets are not available to components or applications that only require non-sensitive information. This is directly aligned with preventing a chatbot from accessing or mixing confidential data with general conversational content.
NEW QUESTION # 366
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 # 367
A clinic has deployed a new AI application that processes protected health information. An AI security consultant raised concerns about validating transparency and traceability. Which tool BEST provides the information needed to address these concerns?
Answer: C
Explanation:
Model cards document key details about an AI model, including its design, training data, performance, and limitations, providing the transparency and traceability needed to understand how the model operates and makes decisions.
NEW QUESTION # 368
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 # 369
Which of the following is the BEST source of information for AI developers to review when selecting an appropriate machine learning (ML) model and identifying key risk?
Answer: B
Explanation:
AI model cards provide structured information about a machine learning model's intended use, performance characteristics, limitations, risks, ethical considerations, and testing results. This makes them the best source for developers when selecting appropriate models and identifying associated risks.
NEW QUESTION # 370
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