OpenText Behavioral Signals and Splunk User Behavior Analytics are competitors in the data analysis sector. Splunk holds an advantage with its comprehensive features, though OpenText excels in pricing and support.
Features: OpenText Behavioral Signals uses AI to analyze conversations, identify sentiment, and detect emotional patterns. Splunk User Behavior Analytics focuses on robust anomaly detection, offering insights into security threats. OpenText centers on communication insights, while Splunk is dedicated to security analytics.
Room for Improvement: OpenText could enhance its scalability and integration capabilities. There is room to improve real-time analytics for more immediate insights. Expanding AI capabilities could help in more precise sentiment analysis. Splunk might benefit from simplifying its user interface for non-technical users. Enhancing its customer support with personalized options could also improve user satisfaction. Reduced costs or tiered pricing models can make the solution more accessible to smaller enterprises.
Ease of Deployment and Customer Service: OpenText Behavioral Signals is known for straightforward deployment along with robust support channels, ensuring a smooth onboarding process. Splunk User Behavior Analytics offers a more complex setup but provides extensive technical documentation, although its customer support is less personalized.
Pricing and ROI: OpenText Behavioral Signals offers a competitive pricing model, potentially yielding high ROI through improved customer interaction and satisfaction. Splunk User Behavior Analytics has a higher initial cost but promises substantial long-term value through its extensive features and security improvements.
The solution can save costs by improving incident resolution times and reducing security incident costs.
The support quality is excellent for paid tiers, following enterprise-grade SLAs with proactive support and deep expertise.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
I would rate the support at eight, meaning there's some room for improvement.
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 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.
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.
Comparing with the competitors, it's a bit expensive.
The pricing is based on the amount of data processed, and it is considered a high-level investment for enterprises.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
Splunk User Behavior Analytics is known for its advanced analytics and data correlation capabilities, which help in detecting patterns, anomalies, and security threats.
The best features in Splunk User Behavior Analytics include anomaly detection, behavioral profiling, and risk scoring and prioritization functionality.
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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