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ISACA Advanced in AI Audit Questions 241–245 (page: 50)

Updated On: 12-Sep-2026

An IS auditor is assessing an AI-powered chatbot that produces inaccurate or fabricated information for customer queries.
Which of the following is the BEST recommendation when the model’s configuration emphasizes creativity over accuracy?

  1. Add more randomness through external prompts.
  2. Allow the AI model to self-correct its hallucinated outputs.
  3. Implement stricter prompt templates to constrain the model's outputs.
  4. Adjust the model’s temperature parameter to lower randomness.

Answer(s): D

Explanation:

Adjusting the model’s temperature parameter to lower randomness is the best recommendation. Lowering temperature reduces creative variability, making the chatbot more deterministic and accurate in its responses, which directly addresses the issue of fabricated information.



An AI-driven incident management system determines the root cause of a security event by:

  1. correlating similar events across multiple systems.
  2. flagging previously reported vulnerabilities.
  3. automatically applying patches to all systems on the network.
  4. reviewing logs and flagging the first system to report anomalous events.

Answer(s): A

Explanation:

Correlating similar events across multiple systems allows the AI-driven incident management system to identify common patterns and pinpoint the root cause of a security event more effectively than isolated analysis.



Which of the following metrics should an IS auditor recommend as a key performance indicator (KPI) to assess the reliability of a natural language processing AI model?

  1. Landmark localization error
  2. Receiver operating characteristic curve
  3. Recall-oriented understudy for gisting evaluations
  4. Mean squared error

Answer(s): C

Explanation:

Recall-Oriented Understudy for Gisting Evaluations (ROUGE) is the appropriate KPI for assessing the reliability of a natural language processing (NLP) model. ROUGE measures the quality of text generation by comparing the model’s output with reference texts, making it suitable for evaluating NLP reliability.



The PRIMARY objective of conducting a red team simulation to test the security of an AI model is to:

  1. verify the availability of the infrastructure under adverse conditions.
  2. validate the accuracy of the model's outputs across multiple data sets.
  3. test the robustness of the model’s predictions under unexpected conditions.
  4. identify weaknesses in the model's ability to resist adversarial inputs.

Answer(s): D

Explanation:

The primary objective of a red team simulation for an AI model is to identify weaknesses in the model's ability to resist adversarial inputs. This helps uncover vulnerabilities that could be exploited to manipulate or degrade the model’s performance.



Which of the following assessments of data quality would an IS auditor use to validate relationship completeness between large data sets?

  1. Counterfactual analyses
  2. Drift detection algorithms
  3. Referential integrity checks
  4. Cross-column profiling

Answer(s): C

Explanation:

Referential integrity checks are used to validate relationship completeness between large data sets. They ensure that linked data across tables or sources is consistent and accurate, which is essential for maintaining reliable AI model training and outputs.



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