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The future works with actionable insights
Organizations have more rich data available to them than ever before, but data is only as good as the insights it provides and the actions you can take on it. Gathering this data can be unwieldy and making sense of it can be overwhelming. With machine learning applied to data that spans network traffic, users, files, and endpoints, you can identify and take action on user behavior and app performance.
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.
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