Detect compromised accounts or insider threats using AI-powered analysis of behavioral anomalies and access patterns.

User Behavior Analytics (UBA) powered by AI has become a critical layer of modern cybersecurity, enabling organizations to detect compromised accounts and insider threats with far greater accuracy than traditional monitoring tools. Instead of relying solely on static rules or signatures, AI-driven UBA continuously learns normal user behavior—such as login times, device usage, data access patterns, and communication habits—and establishes a dynamic baseline for each individual. When deviations occur, such as unusual login locations, excessive data downloads, or unauthorized attempts to access sensitive files, the system immediately flags these anomalies as potential security risks. This proactive approach helps identify not only external attackers who have stolen credentials but also malicious or careless insiders whose activities may otherwise go unnoticed. By analyzing behavioral anomalies in real time, AI-powered UBA provides early warning signals before threats escalate into full-scale breaches. Additionally, it reduces false positives by differentiating between legitimate exceptions and genuine risks, allowing security teams to focus on the most critical incidents. Ultimately, User Behavior Analytics strengthens organizational defenses by offering visibility into hidden patterns, uncovering threats from within, and providing the intelligence needed to act quickly and decisively.