Which of the following key performance indicators (KPIs) are MOST important when evaluating whether an AI model meets business objectives?
Answer(s): B
The most important KPI when evaluating whether an AI model meets business objectives is AI model accuracy in predicting actual outcomes. High accuracy indicates that the model is effectively supporting decision-making and delivering value aligned with business goals.
The BEST way to prevent sensitive information disclosure by large language model (LLM) chatbots is through:
The best way to prevent sensitive information disclosure by LLM chatbots is through data sanitization. This process involves removing or modifying sensitive data before it is used by the model, ensuring that personally identifiable or confidential information cannot be learned or reproduced by the chatbot.
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?
Answer(s): D
The greatest risk is disclosure of personal information. Using customer account data to train an AI chatbot can lead to unintended exposure of sensitive or identifiable information if proper data protection and privacy safeguards are not enforced.
An IS auditor is evaluating an organization’s incident management program to ensure it is sufficiently prepared to manage AI-related incidents. Which of the following is MOST important for the auditor to validate?
Answer(s): A
The most important aspect for the IS auditor to validate is that the program includes processes to respond to AI model drift and data integrity attacks. These are critical AI-specific risks that can compromise decision-making accuracy and system reliability, so preparedness to detect and respond to them is essential for robust incident management.
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?
The most important consideration for identifying AI risk in a KYC application is the business disruption and financial impact. In banking, KYC failures due to AI errors can lead to compliance violations, reputational damage, and significant financial penalties. Evaluating the potential for disruption and associated costs is critical for effective AI risk assessment in this context.
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