ISACA AAISM Exam Prep
ISACA Advanced in AI Security Management (Page 13 )

Updated On: 13-Sep-2026

Personal data used to train AI systems can BEST be protected by:

  1. anonymizing personal data.
  2. hashing personal data.
  3. erasing personal data after training.
  4. ensuring the quality of personal data.

Answer(s): A

Explanation:

Anonymization removes or irreversibly masks personally identifiable information (PII) in datasets, preventing individuals from being re-identified. This is the most effective method for protecting personal data in AI training while allowing the model to learn patterns from the data.



Embedding unique identifiers into AI models would BEST help with:

  1. preventing unauthorized access.
  2. detecting adversarial attacks.
  3. tracking ownership.
  4. eliminating AI system biases.

Answer(s): C

Explanation:

Embedding unique identifiers (e.g., watermarks or fingerprints) into AI models provides a verifiable link to the creator or owner. This facilitates intellectual property protection, accountability, and traceability of the model, especially in cases of unauthorized use or distribution.



Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?

  1. Health support system
  2. Credit risk modeling system
  3. Car navigation system
  4. Industrial control system

Answer(s): A

Explanation:

Health support systems directly impact patient safety and critical medical decisions. Any downtime can have immediate life-threatening consequences, requiring the shortest possible recovery time objective (RTO) to ensure continuous, reliable operation.



An organization has requested a developer to apply AI algorithms to existing modules in order to improve customer service quality. At this stage, which of the following should be considered FIRST?

  1. IT management may need to revise the service agreement if AI behavior cannot be predefined.
  2. The organization may need to explain the performance of the applied AI algorithm.
  3. Project sponsors may need to agree on a phased approach in order to ensure safe release.
  4. The developer may need to be held accountable for business inquiries raised by customers.

Answer(s): C

Explanation:

Introducing AI into existing systems carries potential risks to functionality, customer experience, and safety. Establishing a phased rollout allows monitoring, validation, and adjustment of AI behavior in controlled stages, minimizing negative impact and ensuring a safe and effective deployment.



Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?

  1. They dynamically generate new labeled data sets.
  2. They analyze patterns in data to group legitimate activity from actual threats.
  3. They learn from historical labeled data.
  4. They use real-time feature engineering to automatically adjust decision boundaries.

Answer(s): C

Explanation:

Supervised learning models are trained on historical data with known outcomes (labeled as benign or malicious). By learning these patterns, the models can more accurately distinguish legitimate activity from threats, reducing false positives in cybersecurity detection.



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