Splunk User Behavior Analytics and ArcSight Analytics compete in threat detection and user behavior analysis. Splunk appears to have an edge with its user-friendly customization and strong integration capabilities, while ArcSight is known for its advanced correlation engine.
Features: Splunk provides powerful data searching, customizable platforms, and seamless integration. ArcSight excels with stability, scalability, and insightful threat analysis.
Room for Improvement: Splunk can improve integration capabilities, simplify pricing models, and enhance network behavior analytics. ArcSight needs better user-friendliness, simpler dashboards, and improved correlation capabilities.
Ease of Deployment and Customer Service: Splunk supports both on-premises and public cloud deployments with satisfactory customer service. ArcSight offers on-premises and hybrid cloud options, though its customer service can be complex.
Pricing and ROI: Splunk is expensive with unpredictable pricing and high licensing costs but offers productivity gains. ArcSight also has high costs based on environment size and maintenance, yet both promise ROI in productivity and time savings.
| Product | Mindshare (%) |
|---|---|
| Splunk User Behavior Analytics | 5.7% |
| ArcSight Analytics | 2.0% |
| Other | 92.3% |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 4 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 6 |
| Large Enterprise | 12 |
ArcSight User Behavior Analytics offers enterprises the ability to monitor and detect from internal and external security threats and fraud.
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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