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Splunk User Behavior Analytics (Splunk UBA)

Splunk UBA is a machine learning driven solution that helps organizations find hidden threats and anomalous behavior across users, devices, and applications. Its data science driven approach produces actionable results with risk ratings and supporting evidence, augmenting SOC analysts’ existing techniques. In addition, it provides visual pivot points for hunters to proactively investigate anomalous behavior.

• Detects insider threats using out-of-the-box purpose-built 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)
• Increases SOC efficiency with rank-ordered threats and supporting evidence
• 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

Splunk User Behavior Analytics (UBA) is built on a big data platform (Hadoop) that horizontally scales and analyzes behavior of hundreds-of-thousands of users, devices and applications. It process data generated from various technologies: network, endpoint, identity, cloud, and applications, to identify anomalous behavior, and stitch applicable anomalies into threat(s) using its multi-pass machine learning architecture.

Splunk UBA visualizes the threat over a kill-chain, thereby, providing contextual awareness, along with supporting evidence for SOC analyst to consume.

Splunk UBA provides organizations the ability to:

• Enhance detection footprint by using behavior centric approach
• Augment SOC analyst by automatically stitching hundreds of anomalies into a single threat
• Provide enhanced context by visualizing the threat across multiple phases of an attack

For more information see the Splunk UBA Home Page and Splunk User Behavior Analytics Documentation

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