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NEW QUESTION # 217
Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?
Answer: D
Explanation:
AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts.
Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.
References:
AAISM Study Guide - AI Technologies and Controls (Robust Training Data Strategies) ISACA AI Security Management - Biometric AI Security Risks
NEW QUESTION # 218
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?
Answer: D
Explanation:
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.
NEW QUESTION # 219
During the deployment of a generative AI platform, a risk assessment identified threats related to data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?
Answer: C
Explanation:
Mapping identified AI threats to enterprise control catalogs and integrating AI-specific safeguards ensures that controls are selected based on the organization's existing risk management framework while also addressing unique AI-related risks such as prompt manipulation and data leakage. This provides comprehensive, risk-aligned, and governance-consistent protection.
NEW QUESTION # 220
Which of the following is the MOST effective use of AI in incident response?
Answer: C
Explanation:
AAISM's risk management guidance notes that the most effective application of AI in incident response is in automating triage activities. AI systems can rapidly analyze logs, alerts, and telemetry to prioritize incidents, reducing response times and allowing human analysts to focus on critical issues. Streamlining testing and improving playbooks are valuable but secondary benefits. Ensuring chain of custody is critical for legal admissibility of evidence but is primarily a human and process-driven control, not AI's strength. The greatest efficiency and effectiveness comes from AI-driven triage automation.
NEW QUESTION # 221
An AI fraud detection system frequently flags legitimate transactions as suspicious, leading to customer dissatisfaction and operational obstacles. Which of the following is the BEST way to mitigate this issue without compromising security?
Answer: D
Explanation:
Conducting a model review helps identify why legitimate transactions are being incorrectly flagged and enables improvements to model accuracy. Adding explainability techniques also helps analysts and stakeholders better understand decision outcomes, reducing operational issues while maintaining effective fraud detection capabilities.
NEW QUESTION # 222
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