The login fires. A query hits the database. The screen shows nothing unusual, but Phi User Behavior Analytics already knows something has changed.
Phi User Behavior Analytics is built to track, map, and analyze every action across your systems. It captures patterns in authentication, transaction flows, API calls, and admin activity. It builds profiles of normal behavior, then flags anomalies in real time. You get clear signals with no noise.
This isn’t just logging. It’s an engine that parses raw event streams into behavior models. By clustering related events — logins, endpoint hits, permission changes — Phi UBA creates a timeline of how your users move through applications. It identifies deviations: unexpected IP shifts, abnormal request rates, privilege escalations. You see the sequence, not just the isolated event.
Phi User Behavior Analytics works across microservices, distributed databases, and serverless workflows. It scales with your architecture. Whether you run Kubernetes pods or edge functions, the analytics stay precise. Latency is measured in milliseconds, so alerts arrive before the breach spreads.