Gurucul UEBA vs Splunk User Behavior Analytics comparison

Threats are a moving target. Determined and persistent threat actors purposely stretch out their activity across weeks or even months, especially when most SIEM and XDR solutions are incapable of piecing together events across time. Even worse, is that these solutions primarily use rule-based Machine Learning, which is essentially pattern matching. This makes them especially ineffective in detecting new attacks and/or variants, which are highly successful in breaching organizations. Discover how Gurucul UEBA security can help your enterprise.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.