AI Fundamentals

Behavioral AI (often implemented as User and Entity Behavior Analytics, or UEBA) works by establishing a baseline of normal user behavior patterns and then using machine learning to flag deviations from that baseline as potential anomalies. This makes it directly suited for detecting unusual or suspicious employee actions, such as insider threats, compromised credentials, or policy violations. In contrast, deep neural networks are a general-purpose machine learning architecture used across many domains and are not specifically the tool for behavioral anomaly detection, and a confusion matrix is simply a performance-evaluation table used to assess the accuracy of a classification model (showing true/false positives and negatives) — it does not itself detect anomalies.