

IBM Security QRadar and Vectra AI are key competitors in the security event and threat detection market. While IBM Security QRadar offers comprehensive SIEM capabilities, Vectra AI is more focused on AI-driven network threat detection and visibility throughout the attack lifecycle. Based on the data, Vectra AI has a slight advantage because of its proactive and real-time threat detection capabilities that excel in minimizing alert fatigue.
Features: IBM Security QRadar provides functionalities such as ease of extracting information from raw logs, scalability, automatic log source identification, and a comprehensive set of built-in rules and reports. It is designed for compliance monitoring and offers a holistic view of security events. Vectra AI focuses on detecting and prioritizing network anomalies and threats using AI-driven analytics, providing real-time visibility across the attack lifecycle and offering insights into behavioral patterns.
Room for Improvement: IBM Security QRadar could improve with enhanced incident management features, better graphing capabilities, and more simplified integration with third-party solutions. User interface design could also be improved to handle complex deployments more efficiently. Vectra AI can benefit from improved visibility into host-based activities and better correlation of detections across systems. Enhanced integration with varied data sources and more proactive threat intelligence offerings are also recommended areas for growth.
Ease of Deployment and Customer Service: IBM Security QRadar supports various deployment models, including on-premises, public cloud, and hybrid cloud, and offers a robust support network. However, its technical support receives mixed feedback regarding consistency. Vectra AI is versatile in deployment options and garners positive feedback for its prompt and knowledgeable support services.
Pricing and ROI: IBM Security QRadar is considered a premium product, with pricing based on events per second, making it potentially costly for smaller enterprises. Its pricing reflects its feature richness, but it is seen as expensive versus some competitors. Vectra AI also has a high price tag, justified by its advanced AI capabilities and comprehensive threat detection features. Both solutions are considered to provide good ROI, with IBM Security QRadar being more suitable for larger enterprises, while Vectra AI is valuable for security environments focusing on advanced threat detection and mitigation.
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 | 3.0% |
| Other | 93.8% |

| Company Size | Count |
|---|---|
| Small Business | 91 |
| Midsize Enterprise | 39 |
| 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 offers advanced hybrid network and identity security, detecting threats traditional tools miss. It uses AI to identify lateral attacks and credential misuse, providing a proactive defense for enterprises.
Vectra AI enhances security by using AI-driven detection across network, cloud, and identity layers, surpassing EDR and SIEMs by offering real-time threat detection. It ensures continuous observability and automates SOC workflows to minimize manual efforts, creating an efficient security environment. Its AI-powered approach significantly reduces noise, focusing on true threats, and provides insights into complex threat landscapes, with seamless integration into environments like EDR and Office 365.
What are Vectra AI's key features?Vectra AI is utilized across industries for comprehensive network and anomaly detection. Organizations deploy it for threat hunting and incident response, monitoring both on-premises and cloud activities. By placing sensors across sites, they optimize security practices and streamline their detection processes.
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