IBM Security QRadar and Vectra AI are both leaders in the cybersecurity market. However, Vectra AI appears to have an advantage with its focus on advanced AI and network insights, providing in-depth threat visibility, while IBM QRadar offers robust integration capabilities and Watson integration but has room for improvement in certain areas.
Features: IBM Security QRadar offers integration ease, rule management, and Watson AI for advanced threat detection. Its dashboards provide network traffic insights, and it supports multiple deployment scenarios for flexibility. Vectra AI excels with its advanced AI models, providing detailed user behavior analysis and risk scores that aid in prioritizing threats based on detection patterns.
Room for Improvement: IBM QRadar could improve its dashboard usability and simplify integration processes. Enhancing search capabilities and optimizing threat intelligence would also benefit users. Vectra AI could enhance integration flexibility, improve interaction with threat feeds, and tackle issues with metadata management and false positives.
Ease Of Deployment and Customer Service: IBM Security QRadar supports various deployment models including on-premises and hybrid, but setup complexity may challenge users. Customer service experiences vary in quality and response time. Vectra AI offers similar deployment options and is recognized for consistent customer service, though support quality can vary.
Pricing and ROI: IBM Security QRadar's pricing can be high due to complex EPS-based licensing, with additional costs for more features. Despite this, it delivers strong ROI in security assurance. Vectra AI also has higher pricing but with a simpler licensing model, promising efficiency and ROI.
With SOAR, the workflow takes one minute or less to complete the analysis.
Investing this amount was very much worth it for my organization.
They assist with advanced issues, such as hardware or other problems, that are not part of standard operations.
Support needs to understand the issue first, then escalate it to the engineering team.
The support is really good; for instance, if a critical ticket is submitted, you will get paged right away as it gets logged, and their analyst will look into it, letting you know as soon as possible so you can work on it.
The support is quite reliable depending on the service engineer assigned.
When I create tickets, the response is fast, and issues are solved promptly.
For EPS license, if you increase or exceed the EPS license, you cannot receive events.
I think QRadar is stable and currently satisfies my needs.
The product has been stable so far.
We receive logs from different types of devices and need a way to correlate them effectively.
If AI-related support can suggest rules and integrate with existing security devices like MD, IPS, this SIM can create more relevant rules.
IBM Security QRadar does not support Canvas, so we had to create custom scripts and workarounds to pull logs from Canvas.
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.
Splunk is more expensive than IBM Security QRadar.
It was costly mainly because of the value you can get right now compared to other solutions.
Vectra is cheaper in terms of pricing and features compared to Darktrace.
It is very acceptable when you compare it with Darktrace, for example.
Recently, I faced an incident, a cyber incident, and it was detected in real time.
IBM is seeking information about IBM QRadar because a part of QRadar, especially in the cloud, has been sold to Palo Alto.
We have FortiSOAR and IBM Resilient for IBM Security QRadar orchestration.
There are extensive out-of-box detection capabilities.
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.
| Product | Market Share (%) |
|---|---|
| IBM Security QRadar | 3.5% |
| Vectra AI | 3.4% |
| Other | 93.1% |
| Company Size | Count |
|---|---|
| Small Business | 89 |
| Midsize Enterprise | 36 |
| Large Enterprise | 102 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 10 |
| Large Enterprise | 27 |
IBM Security QRadar (recently acquired by Palo Alto Networks) is a security and analytics platform designed to defend against threats and scale security operations. This is done through integrated visibility, investigation, detection, and response. QRadar empowers security groups with actionable insights into high-priority threats by providing visibility into enterprise security data. Through centralized visibility, security teams and analysts can determine their security stance, which areas pose a potential threat, and which areas are critical. This will help streamline workflows by eliminating the need to pivot between tools.
IBM Security QRadar is built to address a wide range of security issues and can be easily scaled with minimal customization effort required. As data is ingested, QRadar administers automated, real-time security intelligence to swiftly and precisely discover and prioritize threats. The platform will issue alerts with actionable, rich context into developing threats. Security teams and analysts can then rapidly respond to minimize the attackers' strike. The solution will provide a complete view of activity in both cloud-based and on-premise environments as a large amount of data is ingested throughout the enterprise. Additionally, QRadar’s anomaly detection intelligence enables security teams to identify any user behavior changes that could be indicators of potential threats.
IBM QRadar Log Manager
To better help organizations protect themselves against potential security threats, attacks, and breaches, IBM QRadar Log Manager gathers, analyzes, preserves, and reports on security log events using QRadar Sense Analytics. All operating systems and applications, servers, devices, and applications are converted into searchable and actionable intelligent data. QRadar Log Manager then helps organizations meet compliance reporting and monitoring requirements, which can be further upgraded to QRadar SIEM for a more superior level of threat protection.
Some of QRadar Log Manager’s key features include:
Reviews from Real Users
IBM Security QRadar is a solution of choice among users because it provides a complete solution for security teams by integrating network analysis, log management, user behavior analytics, threat intelligence, and AI-powered investigations into a single solution. Users particularly like having a single window into their network and its ability to be used for larger enterprises.
Simon T., a cyber security services operations manager at an aerospace/defense firm, notes, "The most valuable thing about QRadar is that you have a single window into your network, SIEM, network flows, and risk management of your assets. If you use Splunk, for instance, then you still need a full packet capture solution, whereas the full packet capture solution is integrated within QRadar. Its application ecosystem makes it very powerful in terms of doing analysis."
A management executive at a security firm says, "What we like about QRadar and the models that IBM has, is it can go from a small-to-medium enterprise to a larger organization, and it gives you the same value."
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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