An IS auditor finds that an AI model ' s outputs are not being reviewed.

When AI outputs are not being reviewed , the primary concern is that incorrect, biased, or non-compliant decisions may go undetected. The best way to address this risk is to implement a validation process for AI decisions (B), which may include human-in-the-loop checks, sampling-based reviews, escalation criteria, and formal approval workflows. AAIA emphasizes the importance of supervision and validation of AI outputs , particularly where decisions have financial, legal, or safety implications.

A larger training dataset (A) does not guarantee correctness or fairness and does not replace oversight. Regular retraining (C) can be beneficial but can also propagate errors if not validated. Prompt templates (D) are relevant mainly for generative and prompt-based systems, and they do not constitute systematic review of outputs. Therefore, a structured validation process is the most direct and effective control.