Isaca AAIA Dumps Questions Answers

Clustering, especially in sensitive domains like healthcare, can inadvertently expose confidential patient data if the resulting groups are too specific or reveal underlying health conditions. The AAIA™ Study Guide warns that clustering can increase privacy risks when small, homogenous groups are formed that effectively re-identify individuals or reveal sensitive traits.

“Clustering results must be carefully reviewed to prevent indirect re-identification or unintended exposure of sensitive traits. Ethical handling of aggregated patient data is essential to protect individual privacy.”

While A and B involve general concerns, and C focuses on performance, D directly addresses the most significant privacy threat: exposure through cluster outputs.

Generative AI excels at synthesizing large datasets and technical documentation into understandable insights. The AAIA™ Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns.

“AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping.”

While summarizing interviews (D) and creating diagrams (B) are helpful, only C directly informs audit planning with actionable intelligence. A (separation of duties) is a later-stage control assessment.

Including AI-specific disruption scenarios in the organization’s Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIA™ Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.

“AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift. Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime.”

Tabletop exercises (A) are valuable for preparedness but are less comprehensive than scenario planning. Kill chains (B) are security-specific. KRIs (C) aid in monitoring but don’t ensure recovery. Therefore, D offers the most robust mitigation.