Uncover anomalous behavior with our cutting-edge UEBA solutions, employing an analytics-driven approach to modern threat detection. Experience unparalleled visibility into your environment, allowing for the identification of subtle threats before they escalate.
Securonix User and Entity Behavior Analytics (UEBA) uses advanced AI, machine learning, and behavioral analytics to detect insider threats, compromised accounts, and anomalous user activity that traditional log analysis often misses. By combining real-time telemetry with historical behavior patterns, Securonix identifies suspicious behavior, account takeovers, privilege misuse, and lateral movement before attacks escalate. The result is faster, more accurate threat detection, fewer false positives, and greater visibility across users, entities, and identities.
UEBA, or User and Entity Behavior Analytics, is a cybersecurity technology that uses machine learning and behavioral analytics to establish normal patterns of activity for users, accounts, devices, and other entities. It identifies deviations from those patterns that may indicate insider threats, compromised accounts, credential abuse, privilege misuse, lateral movement, or data exfiltration.
Unlike traditional rule-based security monitoring, UEBA evaluates behavior and context over time rather than relying only on predefined indicators. Securonix UEBA combines behavioral analytics, machine learning, identity context, threat intelligence, and risk scoring to help security teams identify suspicious activity and prioritize high-risk threats.
Leverage patented machine learning capabilities and out-of-the-box use case content that is trusted by 5 of the Fortune 10 companies.
Extend security monitoring to your cloud environment with built-in APIs for all major cloud infrastructure and application technologies.
Mitigate the risk from insiders with a UEBA that combines events with user context to alert you of behaviors that deviate from established baseline.
Upgrade without having to rip or replace. As a cloud-native solution, our UEBA can be deployed quickly on top of your existing SIEM.
Experience enhanced security with Securonix UEBA, utilizing cutting-edge machine learning and behavior analytics. Our sophisticated technology significantly reduces false positives, providing a comprehensive understanding of both user and entity-level threats.
As a cloud-native solution, Securonix UEBA can be deployed quickly, enabling you to rapidly respond to threats from outside or within.
Leverage a flexible technology stack that lets you easily upgrade your legacy solution without having to rip and replace your existing SIEM.
Phishing attacks increased 62% over the last year, recently leveraging corporate tools like Microsoft OneNote, and continues to be the leading vector for threats. Read the Threat Report for the top tactics, techniques and procedures and the leading indicators of compromise observed by Securonix Threat Labs.
Learn why HDFC chooses Securonix as their partner to detect complex cyber attacks.
UEBA, or User and Entity Behavior Analytics, analyzes user, account, device, and system activity to establish normal behavioral patterns and identify anomalies that may indicate security threats. It helps security teams detect suspicious activity that traditional rules and signatures may miss.
UEBA can help detect insider threats, compromised accounts, credential abuse, privilege misuse, lateral movement, data exfiltration, and other abnormal behavior by identifying deviations from established behavioral patterns.
Traditional SIEM primarily collects, correlates, and analyzes security events across an organization. UEBA adds behavioral analytics and machine learning to identify unusual activity involving users and entities, providing additional context and risk signals for threat detection and investigation.
UEBA continuously evaluates behavior across users and entities to identify changes in access patterns, authentication activity, privilege usage, data movement, and other risk indicators. This can expose malicious insiders and compromised accounts even when attackers are using legitimate credentials.
UEBA combines behavioral anomalies, contextual signals, and risk scoring to help SOC analysts prioritize activity most likely to represent a genuine threat. This allows teams to focus investigations on higher-risk users and entities instead of treating every individual alert with equal urgency.
Security teams should evaluate behavioral analytics depth, machine learning capabilities, entity profiling, dynamic risk scoring, integration with SIEM and security data sources, investigation context, scalability, and the ability to detect complex threats across users, identities, devices, and systems.
As AI agents take on more autonomous work, behavioral analytics must expand beyond monitoring human users and traditional entities. Agent and Entity Behavior Analytics (AEBA) extends the principles of UEBA to AI agents, helping security teams establish normal agent behavior, identify unusual access or tool usage, and detect when trusted agents begin operating outside expected patterns. Securonix is expanding UEBA into AEBA to bring human, entity, and AI agent behavior into a unified security model.
