The AAIA™ Study Guide recommends using validation tools to fine-tune and evaluate ML models, particularly when high false positives undermine operational efficiency. ML validation can identify threshold adjustments, retraining needs, or feature misweighting contributing to excessive alerting.

“Model validation enables organizations to quantify performance, reduce false alarms, and recalibrate AI behavior to align with operational needs and threat landscapes.”

While logs (A) and benchmarks (B) help with diagnosis, they don’t improve the model. Penetration testing (C) evaluates detection, not alert noise. D is the most effective solution.