OpenText Behavioral Signals and Splunk User Behavior Analytics compete in the user behavior monitoring and analysis category. Splunk has an upper hand with its comprehensive feature set, despite OpenText being praised for pricing and support.
Features: OpenText Behavioral Signals provides real-time behavioral analysis, proactive incident management, and advanced integration capabilities, while Splunk User Behavior Analytics offers predictive analytics, robust anomaly detection with machine learning, and comprehensive security insights.
Room for Improvement: OpenText could expand its predictive analytics and enhance machine learning capabilities. Technical scalability is another area to consider. Splunk needs improvement in ease of deployment, user interface for non-technical users, and addressing high resource usage during analysis.
Ease of Deployment and Customer Service: OpenText Behavioral Signals offers a streamlined deployment process with dedicated support, facilitating quick setup. Splunk User Behavior Analytics demands a structured deployment but benefits from broader support infrastructure, suitable for extensive integration.
Pricing and ROI: OpenText Behavioral Signals typically requires a lower initial investment, supporting cost-effective budgets with faster ROI. Splunk User Behavior Analytics, though initially more expensive, offers outstanding long-term ROI through its extensive feature suite and advanced threat detection.
The solution can save costs by improving incident resolution times and reducing security incident costs.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
I would rate their technical support as 8.5 out of 10.
From the responsiveness perspective, Splunk is very responsive with SLA-bound support for premium tiers.
Splunk User Behavior Analytics is highly scalable, designed for enterprise scalability, allowing expansion of data ingestion, indexing, and search capabilities as log volumes grow.
With built-in redundancy across zones and regions, 99.9% uptime is achievable.
Splunk User Behavior Analytics is highly stable and reliable, even in large-scale enterprise environments with high log injection rates.
Splunk User Behavior Analytics is a one hundred percent stable solution.
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.
The pricing is based on the amount of data processed, and it is considered a high-level investment for enterprises.
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.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
The best features in Splunk User Behavior Analytics include anomaly detection, behavioral profiling, and risk scoring and prioritization functionality.
| Product | Market Share (%) |
|---|---|
| Splunk User Behavior Analytics | 6.8% |
| OpenText Behavioral Signals | 2.2% |
| Other | 91.0% |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
OpenText Behavioral Signals enhances organizational security monitoring with its robust correlation engine and streamlined dashboard, offering customization to suit different environments like airports or banks.
OpenText Behavioral Signals effectively integrates device logs through its strong correlation engine. The platform's customization options enable tailored alerts to match specific use cases, such as airports or banks. Although it needs more frequent updates to stay aligned with global incidents, it provides a centralized dashboard that ensures comprehensive visibility across networks. Users find the interface intuitive, making rule writing and report access easy, aiding in a comprehensive understanding of the network environment.
What are the key features of OpenText Behavioral Signals?In industries like banking and airports, OpenText Behavioral Signals is implemented for gathering global intelligence from the cloud. It notifies organizations about global attacks and updates its correlation engines. These industries utilize the platform for monitoring and analyzing logs from network devices, security log management, and addressing network challenges like link failures and unauthorized login attempts, ensuring better security posture with behavioral analytics and log integration using Unix and Microsoft-based connectors.
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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