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NEW QUESTION # 203
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:
Answer: B
Explanation:
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
NEW QUESTION # 204
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?
Answer: A
Explanation:
AAISM guidance states that when adopting AI, the most important step is to conduct a risk assessment and update the enterprise risk register. This ensures AI-specific risks are identified, documented, and integrated into the organization's existing governance structures. Benchmarking peers provides context but does not address internal risk. Implementing methodologies and frameworks are important, but they precede or follow the assessment process. The decisive step that connects adoption to enterprise risk governance is updating the risk register with AI-specific risks.
References:
AAISM Study Guide - AI Risk Management (Integration with Enterprise Risk Management) ISACA AI Security Management - Risk Assessment and Register Updates
NEW QUESTION # 205
Secure aggregation enhances the security of federated learning systems by:
Answer: B
Explanation:
Secure aggregation cryptographically aggregates client updates so that the server learns only the sum/aggregate, not any single client's update. Properly implemented, the server cannot recover individual contributions--even if compromised--thereby preserving client confidentiality.
NEW QUESTION # 206
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: D
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 # 207
When using data augmentation to balance an underrepresented class in a training dataset, which of the following is the GREATEST long-term risk to the AI model's real-world performance?
Answer: C
Explanation:
Data augmentation can introduce synthetic patterns that the model may learn too specifically, leading to overfitting. This reduces the model's ability to generalize to real-world data, increasing the likelihood of unexpected failures in production.
NEW QUESTION # 208
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