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NEW QUESTION # 344
Which of the following strategies BEST ensures generative AI tools do not expose company data?
Answer: A
Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input-governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow-lists for approved contexts provides the highest assurance of preventing exposure.
References:* AI Security Management™ (AAISM) Body of Knowledge: Data loss prevention for AI; prompt
/input controls; approved channels and guardrails for generative AI.* AI Security Management™ Study Guide: Preventive over detective controls for confidentiality; enterprise guardrails at prompt capture and egress points.
NEW QUESTION # 345
Security and assurance requirements for AI systems should FIRST be embedded in the:
Answer: D
Explanation:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure-by-design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
NEW QUESTION # 346
During the deployment of a generative AI platform, a risk assessment highlighted threats such as data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?
Answer: C
Explanation:
AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI- specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for-purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.
References: AI Security Management™ (AAISM) Body of Knowledge - Governance & Program Controls; Control Selection and Tailoring; Threat-to-Control Mapping for AI Systems; Risk Appetite & Control Assurance Alignment.
NEW QUESTION # 347
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
Answer: A
Explanation:
In the AI data lifecycle, converting raw user information into model-readable form (e.g., cleaning, parsing, feature engineering, encoding, normalization/standardization, and schema alignment) is performed during data preparation. This phase establishes input quality and structure prior to training and inference and may include normalization as one of several preparation steps. Data collection acquires data, minimization limits data to what's necessary, and normalization is a specific technique-not the overarching phase.
References: AI Security Management (AAISM) Body of Knowledge: Data Lifecycle-Preparation and Ingestion Controls; AAISM Study Guide: Data Preparation Activities (cleaning, labeling, feature engineering, encoding) and Quality Gates.
NEW QUESTION # 348
Which of the following is the GREATEST benefit of performing AI security risk assessments?
Answer: B
Explanation:
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.
NEW QUESTION # 349
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