ExtraHop Reveal(x) 360 and LogRhythm UEBA are solutions in network detection and response and user behavior analytics. ExtraHop stands out in real-time threat detection, while LogRhythm leads in comprehensive analytics depth.
Features: ExtraHop provides advanced threat detection, seamless integration with existing security tools, and enhances overall security ecosystems. LogRhythm offers in-depth user behavior analytics, deep insights into network behavior, and detailed threat analysis.
Room for Improvement: ExtraHop could benefit from offering more granular customization capabilities, improved integration features, and enhanced user interface options. LogRhythm might address system alerting mechanisms to reduce false positives, provide more streamlined integration tools, and enhance dashboard user experience.
Ease of Deployment and Customer Service: ExtraHop is noted for a straightforward deployment process and supportive customer service, suitable for teams with limited resources. LogRhythm, however, requires significant technical expertise for deployment, though post-deployment customer support is generally reliable.
Pricing and ROI: ExtraHop's initial costs are perceived as high, yet users experience substantial ROI through improved threat visibility and response times. LogRhythm is recognized for competitive pricing and good ROI, driven by its detailed analytics capabilities.
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.
LogRhythm UEBA enables your security team to quickly and effectively detect, respond to, and neutralize both known and unknown threats. Providing evidence-based starting points for investigation, it employs a combination of scenario analytics techniques (e.g., statistical analysis, rate analysis, trend analysis, advanced correlation), and both supervised and unsupervised machine learning (ML).
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