How it works

Detect advanced threats and anomalous behavior using machine learning

Splunk User Behavior Analytics uses unsupervised machine learning algorithms to establish baseline behaviors of users, devices and applications, then searches for deviations to detect unknown and insider threats.

Enhance security visibility so you can act decisively

Splunk User Behavior Analytics visualizes threats across multiple phases of an attack to give security analysts a comprehensive understanding of attack root cause, scope, severity and timelines. This context-rich view enables analysts to rapidly assess impact, and make informed decisions quickly and confidently.

Simplify incident investigations to increase SOC efficiency

Splunk User Behavior Analytics automatically reduces billions of raw events down to tens of threats for rapid review, without the need for time-consuming human-fueled detective work performed by an army of highly skilled security and data science professionals.

Features

Uncover the most sophisticated threats.

Streamlined threat workflow

Reduce billions of raw events to tens of threats for quick review and resolution. Use machine learning algorithms to help identify hidden threats without human analysis.

Threat review and exploration

Visualize threats over a kill chain to gain context. Anomalies are stitched together across users, accounts, devices and applications so you can clearly see attack patterns.

User feedback learning

Customize anomaly models based on your organization’s processes, policies, assets, user roles and functions. Get granular feedback to improve confidence in threat severity and detection.

Kill chain detection and attack vector discovery

Detect lateral movement of malware or malicious insider proliferation. See behavior-based irregularities or pinpoint botnet or C&C activity.