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.
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.
Splunk's technical support is amazing.
The support is quite reliable depending on the service engineer assigned.
When I create tickets, the response is fast, and issues are solved promptly.
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.
High data ingestion costs can be an issue, especially for large enterprises, as Splunk charges based on the amount of data processed.
I encountered several issues while trying to create solutions for this advanced version, which seem unrelated to query or data issues.
ExtraHop's ability to decrypt encrypted data is a feature that Vectra AI lacks.
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.
Reserved instances with one or three-year commitments offer lower rates, providing up to 70% savings.
The pricing is based on the amount of data processed, and it is considered a high-level investment for enterprises.
Comparing with the competitors, it's a bit expensive.
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.
I evaluate the automation capabilities for threat detection in Splunk User Behavior Analytics, which uses automated machine learning models and behavioral analytics to detect complex and hidden threats.
The main feature of Vectra AI that I find valuable is its focus on the user interface and its approximately two hundred algorithms based on artificial intelligence and machine learning.
There are extensive out-of-box detection capabilities.
| Product | Market Share (%) |
|---|---|
| Vectra AI | 10.6% |
| Splunk User Behavior Analytics | 2.4% |
| Other | 87.0% |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 10 |
| Large Enterprise | 27 |
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 enhances security operations by pinpointing attack locations, correlating alerts, and providing in-depth visibility across attack lifecycles, ultimately prioritizing threats and improving incident responses.
Vectra AI integrates AI and machine learning to detect anomalies early and supports proactive threat response. Its features like risk scoring, alert correlation, and streamlined SOC efficiency are supplemented by integration with tools like Office 365. Users highlight integration, reporting, and customization challenges, alongside limitations in syslog data and false positive management. They seek enhancements in visualization, UI, TCP replay, endpoint visibility, and tool orchestration, with requests for improved documentation, licensing, and cloud processing innovation.
What are the key features of Vectra AI?In industries like finance, healthcare, and critical infrastructure, Vectra AI is crucial for threat detection and network monitoring. Entities use it for identifying anomalous behaviors and enhancing cybersecurity by responding to network activities and analyzing traffic for potential breaches. It operates on-premises and in hybrid cloud settings, enabling threat detection without endpoint agents and supporting compliance and policy enforcement.
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