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>> Valid AAISM Exam Answers <<
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NEW QUESTION # 345
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?
Answer: A
Explanation:
AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself.
Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.
NEW QUESTION # 346
Which BEST addresses hallucination risk in AI systems?
Answer: D
Explanation:
AAISM states that human oversight is the strongest control for hallucination risks, especially in high-impact decisions. Humans validate outputs, correct errors, and override unsafe model responses.
NEW QUESTION # 347
An aerospace manufacturing company that prioritizes accuracy and security has decided to use generative AI to enhance operations. Which of the following large language model (LLM) adoption plans BEST aligns with the company's risk appetite?
Answer: C
Explanation:
AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for non- critical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety- or mission-critical operations. A public LLM for critical functions (A) is misaligned with a high-assurance posture; buying open-market datasets (B) raises provenance and licensing risk; third-party access (C) can be appropriate but still introduces vendor/visibility limits and data residency concerns that may not meet aerospace security needs.
References: AI Security Management™ (AAISM) Body of Knowledge - Risk Appetite Mapping to AI Use Cases; Criticality Segmentation; Data Control & Deployment Models. AAISM Study Guide - Phased Adoption for High-Assurance Environments; Private vs. Hosted LLM Trade-offs; Governance, Evaluation, and Containment Patterns.
NEW QUESTION # 348
After implementing a third-party generative AI tool, an organization learns about new regulations related to how organizations use AI. Which of the following would be the BEST justification for the organization to decide not to comply?
Answer: A
Explanation:
The AAISM framework clarifies that compliance decisions must always be tied to an organization's risk appetite and tolerance. When new regulations emerge, management may choose not to comply if the associated risk remains within the documented and approved risk appetite, provided that accountability is established and governance structures support this decision. Other options such as widespread industry use, third-party audits, or lack of cost assessment do not justify noncompliance under the governance principles. The risk appetite framework is the only recognized justification under AI governance principles.
NEW QUESTION # 349
Which of the following BEST describes the role of model cards in AI solutions?
Answer: B
Explanation:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of- scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.
References: AI Security Management™ (AAISM) Body of Knowledge - Documentation & Transparency Artifacts; Model Cards for Governance, Risk, and Assurance; Intended Use, Limitations, and Performance Disclosure.
NEW QUESTION # 350
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