Vectra AI and Splunk User Behavior Analytics compete in the cybersecurity and threat detection category. Based on feature comparison, Vectra AI has the edge due to its detailed visibility and effectiveness in correlating security events.
Features: Vectra AI offers granular visibility across the attack lifecycle, effective risk score aggregation, and alert reduction through event correlation. It integrates with SIEM tools to enhance detection accuracy. Splunk User Behavior Analytics provides powerful search capabilities, versatile integration options with other solutions, and strong threat detection customization across data points.
Room for Improvement: Vectra AI could improve integration with external solutions and expand its reporting features for better threat visibility. Its deployment flexibility and operational technology integration need enhancements. Splunk User Behavior Analytics is challenged by its complex licensing model and high costs, which may limit accessibility. Simplifying the licensing process and expanding third-party tool integration could improve user experience.
Ease of Deployment and Customer Service: Vectra AI supports on-premises and hybrid cloud deployments with responsive customer service and regular updates. Splunk User Behavior Analytics is available on-premises and public cloud. Users appreciate its high customization level and integration support, although they report occasional slow response times. Vectra is noted for detailed assistance, while Splunk excels in customization support.
Pricing and ROI: Vectra AI is perceived as costly but effective, with its detailed visibility justifying the investment. ROI is evident in improved security posture and reduced incident response times. Splunk's pricing model is criticized as unpredictable due to enhancements and additional tools. Users perceive value in its comprehensive feature set, with solid ROI demonstrated through enhanced threat detection, though costs may impact wider adoption.
I would rate the support at eight, meaning there's some room for improvement.
The support is quite reliable depending on the service engineer assigned.
Splunk User Behavior Analytics is a one hundred percent stable solution.
Sometimes issues occur when handling long-term data.
I encountered several issues while trying to create solutions for this advanced version, which seem unrelated to query or data issues.
Advanced reporting could see enhancements as there are some issues with latency.
You need to have a Linux server, and from the Linux server, you must perform AI tasks, and there is a lot to be handled in the back end.
Neither Vectra nor Darktrace have a function like a status health check on my log sources and traffic sources.
Vectra is cheaper in terms of pricing and features compared to Darktrace.
It is very acceptable when you compare it with Darktrace, for example.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
Features like alerts and auto report generation are valuable.
There are extensive out-of-box detection capabilities.
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.
Vectra AI is used for detecting network anomalies and potential malicious activities, providing visibility into network traffic and enhancing threat detection across environments.
Organizations deploy Vectra AI mainly on-premises with additional cloud components. It helps with compliance, incident response, security monitoring, detecting insider threats, and correlating network events. Vectra AI captures and enriches network metadata, provides detailed dashboards, reduces false positives, and supports cross-environment behavioral analysis to enhance threat detection and prioritization. While valued for its high accuracy and alert aggregation, it has room for improvement in UI/UX, packet management, and integration with SIEMs and other tools. It is noted for expensive pricing and limited proactive threat response features.
What are Vectra AI's most valuable features?In specific industries, Vectra AI is deployed to monitor complex networks and alleviate challenges in threat detection. It is particularly effective in sectors requiring stringent compliance and security measures, offering insights and capabilities crucial for protecting sensitive data and maintaining operational integrity.
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