Splunk User Behavior Analytics and Securonix UEBA compete in user behavior analytics. Securonix UEBA has the upper hand due to advanced features and value for the expense.
Features: Splunk User Behavior Analytics provides strong data integration, anomaly detection, and reliability for complex data. Securonix UEBA offers advanced threat detection, comprehensive risk scoring, and tailored approaches to security threats.
Ease of Deployment and Customer Service: Splunk User Behavior Analytics allows streamlined deployment and integration into existing systems. Securonix UEBA offers efficient deployment with proactive customer service enhancing the implementation experience.
Pricing and ROI: Splunk User Behavior Analytics has competitive setup costs appealing to budget constraints with significant ROI. Securonix UEBA is more costly but justifies its price with strong ROI due to feature set and effectiveness.
| Product | Market Share (%) |
|---|---|
| Splunk User Behavior Analytics | 6.8% |
| Securonix UEBA | 2.6% |
| Other | 90.6% |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
Securonix User and Entity Behavior Analytics (UEBA) leverages sophisticated machine learning and behavior analytics to analyze and correlate interactions between users, systems, applications, IP addresses, and data. Light, nimble, and quick to deploy, Securonix UEBA detects advanced insider threats, cyber threats, fraud, cloud data compromise, and non-compliance. Built-in automated response playbooks and customizable case management work flows allow your security team to respond to threats quickly, accurately, and efficiently
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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