ISACA AAIA Exam Prep
ISACA Advanced in AI Audit (Page 8 )

Updated On: 10-Sep-2026

Which of the following metrics are the BEST indication of a mature and effective approach to an organization’s data governance program for its AI systems?

  1. Total budget allocated to AI initiatives across all departments
  2. Number of AI projects completed within the last fiscal year
  3. Percentage of AI models with documented data lineage
  4. Frequency of data quality audits on the organization's data sets

Answer(s): C

Explanation:

The percentage of AI models with documented data lineage is the best indication of a mature and effective data governance program. It demonstrates transparency, traceability, and accountability in how data flows through AI systems, which is essential for compliance, auditing, and maintaining trust in AI outputs.



Which of the following is the BEST way to support the development and design of high-risk AI systems?

  1. Conduct regular training sessions for users on data privacy.
  2. Ensure the availability of trustworthy data sets.
  3. Regularly back up the AI system’s data to a secure, offsite location.
  4. Implement multi-factor authentication (MFA) for all users accessing the AI system.

Answer(s): B

Explanation:

The best way to support the development and design of high-risk AI systems is to ensure the availability of trustworthy data sets. Reliable, high-quality data is foundational to building AI systems that are accurate, fair, and compliant — especially critical in high-risk applications where errors can have significant consequences.



An organization shares an AI model with external partners. One partner reports that sensitive data has been inadvertently exposed through the model's outputs.
Which of the following is the IS auditor's BEST recommendation?

  1. Retrain the model immediately and implement privacy-preserving techniques.
  2. Disable the shared model and notify partners of the potential breach.
  3. Limit the model's outputs to anonymized results while investigating further.
  4. Audit the data pipelines of all partners to identify the source of the leak.

Answer(s): A

Explanation:

The best recommendation is to retrain the model immediately and implement privacy-preserving techniques. This addresses the root cause — model exposure of sensitive data — by reducing the risk of similar incidents in the future while restoring trust and ensuring compliance with privacy regulations.



Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?

  1. Peer reviewing code that acquires product reviews from social media posts
  2. Hiring a marketing firm to text links to customers requesting product reviews for monetary compensation
  3. Requiring customers to authenticate access to their accounts prior to writing product reviews
  4. Augmenting the unbalanced product review data set with the use of oversampling by the model developer

Answer(s): C

Explanation:

Requiring customers to authenticate access to their accounts prior to writing product reviews helps mitigate the risk of data poisoning by ensuring that only legitimate users contribute to the training data. This reduces the likelihood of competitors injecting fake or malicious content into the dataset used for sentiment analysis.



The PRIMARY purpose of maintaining an audit trail in AI systems is to:

  1. facilitate transparency and traceability of decisions.
  2. analyze model accuracy and fairness.
  3. ensure compliance with regulatory standards for AI.
  4. measure computational efficiency.

Answer(s): A

Explanation:

The primary purpose of maintaining an audit trail in AI systems is to facilitate transparency and traceability of decisions. Audit trails provide a record of how decisions were made, what data was used, and which processes were followed — supporting accountability, explainability, and trust in AI systems.



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