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?
Answer(s): C
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?
Answer(s): B
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?
Answer(s): A
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?
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:
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.
Post your Comments and Discuss ISACA AAIA exam prep with other Community members: