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ISACA Advanced in AI Audit Questions 266–270 (page: 55)

Updated On: 12-Sep-2026

Which of the following should be applied to an AI system but are not typically used in traditional systems?

  1. Controls to protect data privacy
  2. Controls to monitor data poisoning
  3. Controls to prevent data exfiltration
  4. Controls to manage data governance

Answer(s): B

Explanation:

Monitoring for data poisoning is specific to AI systems because attacks that manipulate training data can alter model behavior. Traditional systems do not rely on learned patterns from training data, making this control uniquely critical for AI.



An internal audit department has noticed that AI generated audit reports are generating false conclusions.
Which of the following is the BEST way to correct this issue?

  1. Increase the model context.
  2. Suspend utilization of the tool until resolved.
  3. Decrease the model's creativity score.
  4. Update service level agreements (SLAs).

Answer(s): C

Explanation:

Decreasing the model’s creativity score (often controlled through parameters such as temperature or top-p) reduces randomness in the model’s outputs, making them more factual and consistent. This directly mitigates false conclusions in AI-generated audit reports.



During a risk assessment for an AI system, data drift was identified as a key risk.
Which of the following is the BEST course of action?

  1. Document the risk and implement continuous monitoring.
  2. Retrain the model immediately using the same data set.
  3. Archive the training data and proceed with deployment.
  4. Disable the AI system until risk is eliminated.

Answer(s): A

Explanation:

Continuous monitoring is essential when data drift is a key risk because it allows early detection of changes in data patterns and timely corrective action. Documentation alone is insufficient, and immediate retraining or shutdown is not justified without ongoing analysis.



Which of the following presents the GREATEST risk when an organization deploys a machine learning (ML) model in a public cloud environment to deliver real-time predictions?

  1. Cloud provider employees have limited skills and experience with AI development.
  2. AI model audit trails have not been comprehensively documented.
  3. The service level agreement (SLA) does not include network latency and inference guarantees.
  4. The cloud provider has not adopted an ethical AI governance framework.

Answer(s): C

Explanation:

For real-time prediction workloads, performance reliability — especially network latency and inference-time guarantees — is critical. Without these guarantees in the SLA, the model may fail to deliver timely or accurate predictions, posing the greatest operational risk to the deployed ML system.



What should be done FIRST when an AI-powered chatbot starts giving incorrect financial advice after a backend API change?

  1. Push a patch to improve chatbot response speed.
  2. Add more rules to override the model's output.
  3. Retrain the model with historical and updated data.
  4. Suspend the chatbot and assess the impact.

Answer(s): D

Explanation:

When a backend API change causes incorrect financial advice, the first priority is to stop the chatbot from providing harmful outputs. Suspending the chatbot prevents further incorrect or risky recommendations while the organization assesses the cause and determines corrective actions.



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