Isaca AAIA Exam Dumps

The GREATEST concern is when the AI system's decision-making process is unexplainable (A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.

Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is the foundational ethical risk.

ISACA, AAIA Exam Content Outline -- Domain 5: Ethical and Legal Considerations in AI (explainability, accountability).

An AI usage policy sets the foundation for safe, ethical, and effective AI deployment. According to the AAIA Study Guide, having an AI policy in place ensures that users understand acceptable behaviors, limitations, and responsibilities when interacting with AI tools.

''AI acceptable use policies promote governance by clearly outlining the dos and don'ts of AI interaction, preventing misuse and aligning user activity with organizational values and compliance standards.''

Other actions (B, C, D) are important in operations and risk management but should follow the establishment of governance protocols through a usage policy. Hence, A is the highest-priority prerequisite.

In the case of a potential data exposure through AI model outputs, the first and most responsible action from an auditing and risk standpoint is to halt further risk propagation. According to the AAIA Study Guide, immediate containment is vital, especially when regulatory and reputational risks are high.

''Upon detection of a data breach risk, AI models should be immediately disabled from public or partner use, and all relevant parties should be notified as part of a responsible disclosure and containment strategy.''

While options A and D are longer-term remediation steps and B is investigative, none of them provide the urgent containment that is best practice in such a breach context.

Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.

''Precision is the key metric when false positives have a significant operational cost. It provides insight into the model's ability to avoid incorrect positive classifications.''

Accuracy and recall give broader insights, but only precision directly measures false positive risk. Completeness is not a standard ML metric.

The AAIA Study Guide emphasizes that AI implementations introduce dynamic and non-deterministic elements into systems, increasing the risk associated with changes. A comprehensive, AI-specific risk assessment is therefore the most critical component of a change management program to ensure that updates, retraining, or parameter adjustments do not introduce vulnerabilities or unintended consequences.

''Risk assessments tailored to AI are crucial because changes to models, training data, or infrastructure can affect performance, ethical compliance, or expose the system to new threats. A standard IT change review is often insufficient.''

While having a governance committee (A) and reviewing documentation (B) are important supporting practices, only option C directly mitigates the core risks of AI system change. Ethics training (D) supports awareness but is not directly tied to change control.