Splunk User Behavior Analytics and ExtraHop Reveal(x) 360 compete in the field of security analytics. Splunk offers better pricing and support, but ExtraHop excels in features, making it desirable for its cost.
Features: Splunk User Behavior Analytics provides machine learning threat identification, detailed forensic investigations, and user behavior analytics. ExtraHop Reveal(x) 360 offers real-time threat detection, expansive network visibility, and automated threat responses.
Ease of Deployment and Customer Service: ExtraHop Reveal(x) 360's cloud-based deployment enables rapid setup and is backed by strong customer service. Splunk User Behavior Analytics requires more extensive on-premises resources, affecting deployment speed.
Pricing and ROI: Splunk User Behavior Analytics involves higher initial costs, justified by long-term ROI through advanced analytics. ExtraHop Reveal(x) 360 delivers quicker ROI with a predictable pricing model and fast deployment.
Cloud is where your business operates, where it innovates, how it enables employees, and how it connects with customers. Adversaries know this, and that's why attacks against cloud assets in IaaS, PaaS, and SaaS environments are increasing. With Reveal(x) 360, you can mitigate the blast radius of advanced threats like ransomware and supply chain attacks with unified security across multicloud and hybrid environments in a single management pane.
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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