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Compare DNIF vs. IBM QRadar User Behavior Analytics

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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

"The solution is quite stable and offers good performance. It also works on a virtual machine. We haven't found any issues with it so far. It's been reliable."

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"Most of the features are good. It is an excellent solution.""In terms of the most valuable features, the log collections and log processing mechanisms are good. They have good dashboards.""QRadar shows very effective correlations. If you combine all the logins plus user behavior and the current intelligence, it gives a very good correlation for business. I think it reduces the false positives in user activity monitoring because there is a lot of social information to correlate with other data.""It has a powerful GUI where you can put together your use cases, and don't have to write your own scripts.""I really like the feature we have with the logs, that if there are any credit card numbers being used, like a PII, you can just use rejects and you can mask it. This is a really good feature in QRadar.""The feature that I find the most useful is that IBM QRadar User Behavior Analytics is free of charge. It's a fully free product that can be installed on top of IBM QRadar SIEM.""It provides many options for searching. I can see devices from different vendors, like Cisco, in one interface, which is good for me.""Integration is very easy and the reporting is good."

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"The vendor is fairly new and it's not as big as some of the international competitors. It's not a mature product. If you ask them to move data, it might take a lot of time."

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"IMB should reduce the pricing, or reduce some of the features for a more economical solution for the customer.""They have to build more quantitative monitoring, profiling, and make it more predictive.""From a functionality point of view there are issues sometimes.""While the interface is easy to use, it could be a little more responsive.""There should be an extension where we can get the reports. This could be an extension to the dashboard with the Guardian or another product with limited technology, for example IPS. Now, we only have IBM. Basically, it needs more and more integration models.""The user interface and configurability of IBM QRadar User Behavior Analytics can be improved. It has a lot of pre-configured settings and not many things can be changed. It also needs more integrations. Currently, User Behavior Analytics is integrated only with IBM QRadar. It could have deeper integrations. It can also have more complicated scoring models. Currently, it has a very simple linear scoring model for users.""We sometimes get an error about the hard drive. Approximately once in two months, we can't find the logs, and they go missing, which is a terrible issue. We are getting support for this issue from our support company.""The dashboard is pathetic and it takes a long time to perform a search."

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Pricing and Cost Advice
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"The price of this solution is a little bit expensive, so if it were cheaper then it would help.""It's free of charge.""The price of this product is high."

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Also Known As
IBM QRadar UBA, QRadar UBA, QRadar User Behavior Analytics
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DNIF offers solutions to the world’s most challenging cybersecurity problems. Recognized by Gartner and used by some of the well-known global companies like PwC, Vodafone and Tata, this next generation analytics platform combines Security and Big Data Analytics to provide real-time threat detection and analytics to the most critical data assets on the Internet. With over a decade of experience in threat detection systems, DNIF has one of the fastest query response times and bridges the gap between searching, processing, analyzing and visualizing data thereby enabling companies with better SOC (Security Operations Center) management.

The User Behavior Analytics for QRadar (UBA) app is a tool for detecting insider threats in your organization. It is built on top of the app framework to use existing data in your QRadar to generate new insights around users and risk. UBA adds two major functions to QRadar: risk profiling and unified user identities.

Risk profiling is done by assigning risk to different security use cases. Examples might include simple rules and checks such as bad websites, or more advanced stateful analytics that use machine learning. Risk is assigned to each one depending on the severity and reliability of the incident detected. UBA uses existing event and flow data in your QRadar system to generate these insights and profile risks of users.

Learn more about DNIF
Learn more about IBM QRadar User Behavior Analytics
Sample Customers
Vodafone India, IDEA Cellular, RBL Bank, NCDEX, NSE
Information Not Available
Top Industries
Computer Software Company39%
Comms Service Provider21%
Financial Services Firm8%
Comms Service Provider33%
Computer Software Company21%
Insurance Company11%
Financial Services Firm7%
Company Size
No Data Available
Midsize Enterprise29%
Large Enterprise71%
Find out what your peers are saying about One Identity, Cynet, Securonix Solutions and others in User Behavior Analytics - UEBA. Updated: October 2021.
542,029 professionals have used our research since 2012.

DNIF is ranked 13th in User Behavior Analytics - UEBA with 1 review while IBM QRadar User Behavior Analytics is ranked 8th in User Behavior Analytics - UEBA with 8 reviews. DNIF is rated 6.0, while IBM QRadar User Behavior Analytics is rated 6.8. The top reviewer of DNIF writes "Fast and stable but needs better intelligence feeds". On the other hand, the top reviewer of IBM QRadar User Behavior Analytics writes "Stable and solid security intelligence but lacks some functionalities ". DNIF is most compared with Splunk, IBM QRadar, ArcSight Enterprise Security Manager (ESM), ELK Logstash and LogRhythm NextGen SIEM, whereas IBM QRadar User Behavior Analytics is most compared with Splunk User Behavior Analytics, Securonix UEBA, Cynet, LogRhythm Enterprise UEBA and Citrix Analytics.

See our list of best User Behavior Analytics - UEBA vendors.

We monitor all User Behavior Analytics - UEBA reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.