Detecting account compromise with UEBA detection packages

Elastic's InfoSec Threat Detection team employs User Entity Behavior Analytics (UEBA) detection packages to enhance the identification of compromised accounts across their systems. These packages consist of detection rules that collectively generate high-fidelity alerts for anomalous user behavior, minimizing false positives. UEBA models leverage machine learning to discern deviations from established norms in user activity, which can be complex and costly but are effective in detecting unusual actions in environments like Slack, GitHub, and other business systems. By implementing building block rules that monitor new terms or activities, Elastic creates threshold alerts to signal significant deviations, thus improving the accuracy of threat detection without overwhelming security analysts with false alarms. The process involves careful planning and tuning to adapt to each system's unique environment, and the integration of these packages into SIEM enables more efficient monitoring and response to potential security breaches.