

IBM Security QRadar and Kaspersky Anti-Targeted Attack Platform both operate in the cybersecurity domain. Kaspersky has a competitive advantage in threat detection with robust anti-malware technology, while IBM Security QRadar excels in scalability and integration, making it suitable for large enterprises.
Features: IBM Security QRadar provides exceptional threat intelligence, log management, and incident detection, utilizing deep network insights to monitor and secure the environment extensively. It integrates seamlessly with other security products and offers advanced correlation capabilities to streamline threat analysis. In contrast, Kaspersky Anti-Targeted Attack Platform is noted for its comprehensive threat detection, employing behavior analysis and anti-malware capabilities to effectively manage and mitigate advanced threats.
Room for Improvement: IBM Security QRadar may benefit from simplifying its deployment process and reducing configuration complexity, enhancing user experience in large-scale implementations. Improvement in AI-driven analytics could further enhance threat detection capabilities. Kaspersky could improve by increasing its scalability options to better support large enterprises and enhancing integration capabilities with third-party products.
Ease of Deployment and Customer Service: Kaspersky Anti-Targeted Attack Platform is recognized for its streamlined deployment process and responsive customer support, facilitating quick and efficient installation. IBM Security QRadar, while supported by extensive documentation, can involve more complex configurations that demand a higher level of technical expertise, making initial deployment and ongoing management more challenging.
Pricing and ROI: IBM Security QRadar often involves a higher initial investment, offering scalable solutions that can lead to significant ROI, especially in large enterprises. Its extensive capabilities might justify the cost for organizations in need of comprehensive security infrastructure. Conversely, Kaspersky Anti-Targeted Attack Platform delivers a cost-effective initial setup, providing strong protection value, particularly appealing for medium-sized enterprises looking for advanced threat detection at a lower cost.
| Product | Market Share (%) |
|---|---|
| IBM Security QRadar | 1.5% |
| Kaspersky Anti-Targeted Attack Platform | 0.5% |
| Other | 98.0% |


| Company Size | Count |
|---|---|
| Small Business | 91 |
| Midsize Enterprise | 39 |
| Large Enterprise | 105 |
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."
Today’s cybercriminals constantly design unique and innovative methods of penetration and compromise. To avoid perimeter prevention technologies they use social engineering, non-malware and supply chain attacks to operate under the radar of security designed to catch ‘bad’ traces. It’s not enough to just ‘know’ what’s bad or dangerous – enterprises need to understand what’s normal, and use AI-driven techniques that simplify and automate this process. Targeted Attack Analyzer is a machine learning engine that involves self-learning to establish the baseline of normal, legitimate activities of an entire network. Through continuous network telemetry collection it finds deviations, detects suspicious activities and predicts further malicious actions at the initial stages of multilayered attacks.
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