

IBM Security QRadar and Vectra AI compete in the cybersecurity sector, focusing on security information and event management. IBM Security QRadar offers comprehensive features, making it appealing for larger enterprises, whereas Vectra AI provides a strong focus on advanced threat detection through AI.
Features: IBM Security QRadar excels in extracting information from various log sources, utilizing it in searches, reports, and dashboards. It is scalable, easy to install, and supports comprehensive compliance monitoring. Vectra AI focuses on high-level threat detection, reduces alerts through AI, and emphasizes behavioral analysis. It captures network metadata effectively, enriching it with security details.
Room for Improvement: IBM Security QRadar could benefit from enhancing its user experience, particularly in search capabilities, and improving incident management for better alert control. Vectra AI needs improved integration with other systems and better context for certain alerts. Additionally, it lacks comprehensive TCP recording features, which limits monitoring capabilities.
Ease of Deployment and Customer Service: IBM Security QRadar supports multiple deployment models including on-premises and cloud but requires significant resources for setup and management. Customer service is generally strong but response times and expertise could be improved. Vectra AI supports various deployment models and has straightforward deployment, but users desire better integration options and more responsive support.
Pricing and ROI: IBM Security QRadar is seen as expensive but offers robust features with good value for large enterprises. Licensing flexibility is available, though its complexity poses a challenge. Vectra AI is also costly but delivers strong value with its advanced threat detection capabilities. Its pricing may be more accessible for organizations with larger budgets, and both products offer competitive ROI.
With SOAR, the workflow takes one minute or less to complete the analysis.
AWS gives the chance to implement a solution out of the box with use cases that are already in IBM Security QRadar.
I have seen a return on investment; I can share that it includes time saved, money saved, and fewer employees needed.
The payback period is roughly six months.
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.
Customer support receives a rating of nine out of ten due to being very supportive and responding quite efficiently.
For EPS license, if you increase or exceed the EPS license, you cannot receive events.
IBM Security QRadar's scalability is great; you can have a new collector to deploy if you have increased EPS per second.
Vectra AI is scalable because it can work through different kinds of solutions and is compatible with all kinds of cloud solutions.
On cloud, you don't see any disconnections or instability.
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.
All threats, including hacking attempts, should be comprehensively addressed.
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.
It depends on how much you want to spend.
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 Security QRadar gives the opportunity to improve the time to market of the releases with a great evaluation of cybersecurity breaches.
Compared to ArcSight, Splunk, or any other SIEM tools where you need their processing language such as structured query language, SPL, and in Sentinel there is KQL query languages, IBM Security QRadar doesn't require reliance on query languages.
Our company used Vectra AI to detect the malicious threats and viruses before they could cause more damage, and we successfully stopped the threats.
Alert noise was dramatically reduced by nearly 80%, allowing SOC analysts to focus more on true threats, which made them more productive and resulted in higher operational efficiency.
There are extensive out-of-box detection capabilities.
| Product | Market Share (%) |
|---|---|
| IBM Security QRadar | 3.2% |
| Vectra AI | 3.1% |
| Other | 93.7% |

| Company Size | Count |
|---|---|
| Small Business | 89 |
| Midsize Enterprise | 38 |
| Large Enterprise | 105 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 10 |
| Large Enterprise | 29 |
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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