Find out in this report how the two User Entity Behavior Analytics (UEBA) solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Their response time is slow compared to other technical support teams.
I would rate the support at eight, meaning there's some room for improvement.
These problems were not frequent, and the last six to eight months have been stable.
Splunk User Behavior Analytics is a one hundred percent stable solution.
Exabeam needs to improve its documentation and provide more customization for dashboards and case management.
Advanced reporting could see enhancements as there are some issues with latency.
Exabeam's AI capabilities, like the natural language mode, convert natural language into Exabeam queries, enhancing ease of use.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
Exabeam Fusion is a cloud-delivered solution that that enables you to:
-Leverage turnkey threat detection, investigation, and response
-Collect, search and enhance data from anywhere
-Detect threats missed by other tools, using market-leading behavior analytics
-Achieve successful SecOps outcomes with prescriptive, threat-centric use case packages
-Enhance productivity and reduce response times with automation
-Meet regulatory compliance and audit requirements with ease
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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