Splunk User Behavior Analytics and Exabeam compete in the security analytics and user behavior analysis category. Splunk appears to have the upper hand in advanced analytics and integration capabilities, while Exabeam is praised for its usability and effective threat detection.
Features: Splunk User Behavior Analytics is known for its advanced analytics, data ingestion, and customization options, offering substantial flexibility. It is effective in rapid anomaly detection and seamless integration with various platforms. Exabeam excels with a powerful correlation engine, intuitive behavior analytics, and user-friendly interface, providing efficient threat detection and incident response through machine learning and automated workflows.
Room for Improvement: Splunk needs to improve cost-effectiveness and simplify its pricing model, which is often seen as complex and expensive. Enhancements in real-time correlation and third-party integrations are also suggested. Exabeam faces challenges with high false positives and needs faster processing and better integration across environments. Improved documentation and adaptability to client needs would further reduce false positives and enhance security.
Ease of Deployment and Customer Service: Both Splunk and Exabeam support versatile deployment options across on-premises and cloud environments. Splunk users appreciate premium support and rich community resources, while Exabeam users benefit from professional technical support despite suggesting improvements in responsiveness and adaptability.
Pricing and ROI: Splunk's pricing is considered a barrier due to its complexity and high cost, impacting ROI despite its significant capabilities. Exabeam offers a more flexible pricing model, regarded as cost-effective compared to competitors, with reasonable pricing and ROI that justifies its feature set.
Exabeam offers more machine learning models that detect anomalies.
The solution can save costs by improving incident resolution times and reducing security incident costs.
Even with TAM support from Exabeam, many issues go unresolved.
I also had the chance to look at the documentation, and the documentation is good.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
From the responsiveness perspective, Splunk is very responsive with SLA-bound support for premium tiers.
I would rate their technical support as 8.5 out of 10.
Regarding Exabeam's scalability and how well it adapts to its customers' needs, I would rate it an eight.
Splunk User Behavior Analytics is highly scalable, designed for enterprise scalability, allowing expansion of data ingestion, indexing, and search capabilities as log volumes grow.
These problems were not frequent, and the last six to eight months have been stable.
Overall, I think Exabeam's stability level is good.
With built-in redundancy across zones and regions, 99.9% uptime is achievable.
Splunk User Behavior Analytics is a one hundred percent stable solution.
Splunk User Behavior Analytics is highly stable and reliable, even in large-scale enterprise environments with high log injection rates.
Exabeam needs to improve its documentation and provide more customization for dashboards and case management.
I have explored the SaaS version; it offers many new features.
Exabeam's integration capabilities are not good, as Exabeam has a very limited number of integrations and no out-of-box integration.
Global reach allows deployment of apps and services closer to users worldwide, but data sovereignty concerns exist and region selection must align with compliance requirements.
I encountered several issues while trying to create solutions for this advanced version, which seem unrelated to query or data issues.
High data ingestion costs can be an issue, especially for large enterprises, as Splunk charges based on the amount of data processed.
Reserved instances with one or three-year commitments offer lower rates, providing up to 70% savings.
Compared to all other products in the market, it is the most expensive one in all aspects including professional service and licenses, even the cloud version.
Comparing with the competitors, it's a bit expensive.
Exabeam's AI capabilities, like the natural language mode, convert natural language into Exabeam queries, enhancing ease of use.
The product offers useful features like the dashboard, timeline, and session views, which enhance our security tools.
Exabeam's UEBA is the most valuable feature that I have found so far.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
The dashboards themselves are nice, very good, and very helpful, but the accuracy of the data or the information that will be presented on the dashboard is something that needs to be questioned.
Features like alerts and auto report generation are valuable.
| Product | Market Share (%) |
|---|---|
| Exabeam | 7.5% |
| Splunk User Behavior Analytics | 6.0% |
| Other | 86.5% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 4 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
Exabeam Fusion is a cloud-delivered solution that that enables you to:
-Leverage turnkey threat detection, investigation, and response
-Collect, search and enhance data from anywhere
-Detect threats missed by other tools, using market-leading behavior analytics
-Achieve successful SecOps outcomes with prescriptive, threat-centric use case packages
-Enhance productivity and reduce response times with automation
-Meet regulatory compliance and audit requirements with ease
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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