Splunk User Behavior Analytics and LogRhythm UEBA compete in the user behavior analysis and threat detection category. Splunk shows an advantage due to its robust features and usability, despite higher pricing.
Features: Splunk offers excellent search capabilities, rapid data processing, and integration with various systems. Its customizable dashboards and automated reporting are significant features. LogRhythm UEBA features an intuitive dashboard and machine learning capabilities, making it ideal for tracking user behaviors and cyber incidents. It is recognized for straightforward management but has less extensive features compared to Splunk.
Room for Improvement: Splunk could improve its User Behavior Analytics by enhancing tool integration and simplifying configurations. Its pricing model is criticized for being unpredictable and high. LogRhythm could enhance its machine learning features, improve dashboard quality, and expand its use case library. Pricing in certain markets is also a concern.
Ease of Deployment and Customer Service: Splunk supports both on-premises and public cloud deployments, providing flexibility but facing occasional support continuity challenges. LogRhythm primarily operates on-premises, with satisfactory yet average-rated technical support.
Pricing and ROI: Splunk's pricing is considered expensive, with additional costs for integrations and frequent price increases, leading to a moderate ROI due to high initial costs and productivity gains. LogRhythm offers competitive pricing for small to medium businesses but may be expensive compared to solutions like IBM QRadar, with observed ROI benefits in productivity and time savings.
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).
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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