behavioral-analytics

Behavioral analytics detects insider threats and account takeover by establishing baselines for user activity and flagging deviations like unusual login times or data volume spikes. It computes risk scores to prioritize investigation of suspicious entity behavior.

UEBA risk scoring works by applying z-score analysis and peer group comparison to behavioral data, evaluating deviations from established baselines. It aggregates weighted signals into a composite risk score to identify potential account takeover or insider threat patterns.

Setting up behavioral baselines requires event data capturing user and entity activities like login times, locations, and data access volumes. This data establishes normal patterns, enabling the detection of deviations that indicate high-risk account takeover or insider threats.

You cannot fully automate account suspension because the system enforces human approval for mutating actions. It computes risk scores and identifies behavioral anomalies, but a human must review and approve any account suspension or restrictive response.

Unlike static rule-based alerts, behavioral analytics uses peer group comparison and z-score analysis to establish dynamic baselines. This approach detects subtle deviations in user behavior patterns that traditional security tools often miss.

Behavioral analytics establishes baselines for human users, service accounts, and systems. It monitors deviations across all entity types to detect insider threats or account takeovers within your environment.