Isaca Updated AAISM Exam Questions and Answers by yunus

Restoring end user trust during incident handling requires visible, immediate assurance that system outcomes are safe and appropriate. AAISM prescribes human oversight and approval gates for high-risk AI decisions, with human validation of outputs before use as a primary control to maintain trust while technical remediation is underway. Prioritization (A) and monitoring (B) aid operations but do not directly rebuild user confidence in outcomes. Post-incident improvements (D) are essential for long-term assurance but do not provide the immediate trust restoration that supervised, human-validated outputs deliver.

In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.

AAISM stresses that AI tools must align with the organization’s existing control objectives and governance requirements, ensuring consistency with risk management, detection philosophy, and operational processes.

Integration with SIEM (D) is important but secondary. Anomaly detection (B) is a feature, not an architectural requirement. Automated orchestration (A) is optional.

The most effective SOC application of AI is in detecting subtle, hard-to-find attack patterns that reduce false negatives.

AAISM technical control guidance notes that AI in SOCs is best applied to:

Enhance detection accuracy and sensitivity to anomalies.

Assist analysts in identifying hidden patterns that traditional rule-based systems miss.

Augment—not replace—human decision-making for high-confidence outcomes.

Options B and C incorrectly shift responsibility entirely to AI, which contradicts governance principles requiring human oversight. Option D is useful for efficiency, but the primary effectiveness comes from improving detection quality.

Therefore, the most effective use is to reduce false negatives and detect subtle attacks.