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NEW QUESTION # 68
An organization decides to contract a vendor to implement a new set of AI libraries. Which of the following is MOST important to address in the master service agreement to protect data used during the AI training process?
Answer: B
Explanation:
AAISM emphasizes that the right to audit is the most critical contractual safeguard when outsourcing AI services. This allows the contracting organization to independently verify that the vendor is applying appropriate protections to training data, meeting compliance obligations, and upholding privacy requirements. Pseudonymization is a technical method, monitoring is operational, and certifications provide external assurance, but none give the direct, enforceable oversight that audit rights provide. In vendor contracts, the right to audit is the primary safeguard for data protection and governance.
NEW QUESTION # 69
Which testing technique is BEST for determining how an AI model makes decisions?
Answer: A
Explanation:
AAISM indicates that white-box testing allows evaluators full visibility into:
* internal logic
* weights
* decision pathways
* model architecture
This makes it ideal for understanding how decisions are made.
Black box (B) provides no internal visibility. Red/blue team tests (A, D) focus on security, not decision mechanics.
References: AAISM Study Guide - AI Testing; Explainability Through White-Box Analysis.
NEW QUESTION # 70
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?
Answer: B
Explanation:
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation. While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.
References:
AAISM Exam Content Outline - AI Risk Management (Legal and Intellectual Property Risks) AI Security Management Study Guide - Generative AI Data Governance
NEW QUESTION # 71
An organization's business model relies on an AI application that handles sensitive customer information. From a governance perspective, which of the following is the MOST important consideration?
Answer: A
Explanation:
Leadership accountability is the most important governance consideration because governance requires clear ownership, oversight, and responsibility for how AI systems manage sensitive customer information, comply with regulations, and align with organizational risk tolerance and ethical standards.
NEW QUESTION # 72
An aerospace manufacturer prioritizing accuracy and security wants to use generative AI. Which LLM adoption plan BEST aligns with its risk appetite?
Answer: A
Explanation:
AAISM notes that high-security industries (e.g., aerospace) should prefer private, controlled environments with restricted data exposure. Developing a private LLM for non-critical workloads minimizes operational and security risk while enabling innovation.
Public LLMs for critical functions (C) violate safety expectations. Purchased datasets (D) introduce unknown provenance. Outsourcing (B) increases third-party risk.
References: AAISM Study Guide - AI Governance; Safe LLM Adoption Strategies.
NEW QUESTION # 73
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