Elasticsearch vs. IBM Security QRadar SIEM

Elasticsearch is really well suited for searching text (Natural Language Processing) and you can fine tune the searches and scoring very well. I like the ability to find Significant Terms in the Index, where you can find aggregations that are really relevant to a specific search. It also allows for queries to lead to new queries via aggregations which is great for navigating your data. It is less suited to doing more complex aggregations where slices of data are required to be processing using guassian normalizations. And doing searches which join different documents is very very hard, and requires serious thought on how to denormalize data.

Incentivized

QRadar is very well suited on environments where there are not multiple tenants or domains, we do have success on this kind of scenario. IBM Security QRadar SIEM is less appropriate for environments with multiple tenants, specially when each tenant represent a different End Costumer (such as for MSSP companies), those environments require a high amount of rules and building blocks replications, since each tenant will have its own "BB definitions", servers, rules exception, etc. Also, some information, such as EPS count or EPS dropped are generated by QRadar's own log sources, which takes place on default domain, therefore users associated with different domain can not have access to those logs, even when the information is related to other domain's environment. For example, even if Event Collector 1 is associated to Domain A, the log informing its dropped EPS is generated by System notification, log source that must be associated to Default domain.

Incentivized

• Super-fast search on millions of documents. We've got over 2 billion documents in our index and the retrieve speeds are still in the < 1-second range.
• Analytics on top of your search. If you organize your data appropriately, Elasticsearch can serve as a distributed OLAP system
• Elasticsearch is great for geographic data as well, including searching and filtering with geojson, and a variety of geospatial algorithms.

Incentivized

• Enables identification and prioritization of vulnerabilities in IT infrastructure for corrective action.
• Facilitates security incident investigation and forensic analysis.
• Provides a real-time view of security events, enabling immediate incident response.
• Can integrate with external threat intelligence sources to enrich data and improve threat detection.
• Enables the generation of detailed and customized reports.

Incentivized

• Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
• The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
• This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.

Incentivized

• Need to spend more time configuring the system to properly interpret and normalize different type of data collected from multiple resources.
• While Rule creation QRadar uses that rules to detect security threats and generate alerts, but to creating and managing rules is bit complex & tedious work to complete.
• IBM Security QRadar SIEM is excellent in handling large & complex systems that requires in-depth knowledge and extensive training to configure and maintain the system which includes upgrading, optimization of performance & issue troubleshooting.

Incentivized

We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.

Incentivized

With the arrival of IBM Security QRadar SIEM at our company, we have a better vision of all the security needs that may arise, it is a very safe software to use that prevents threats from damaging our IT environment, it is impossible to change it for another software.

Incentivized

To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.

Incentivized

As a grade I give 8 as QRadar is not easy to learn. It requires some time to master it. It also needs a team of people actively working on the product. Once you learn to use it the software works very well and it is easy to correlate and understand detected threats. It only takes time to learn how to use it well and configure it properly.

Incentivized

We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.

Incentivized

Customer support is Good of IBM, While Using IBM QRadar its deployment is to slow and suddenly stop working and crashed we have contacted IBM Support and Rised a Ticket within a few minute we get call back from customer support and Query Resolved by them Fast And Rapid Support of Ibm

Incentivized

The training was very useful and the people who taught us were very knowledgeable. Although the software may initially seem difficult to learn they made things much easier for us.

Incentivized

The training was very useful and the people who taught us were very knowledgeable. Although the software may initially seem difficult to learn they made things much easier for us.

Incentivized

Do not mix data and master roles. Dedicate at least 3 nodes just for Master

Incentivized

Initial patience is required to learn how to use the product, and it takes a dedicated team to use it. One person is not enough, and it's not enough to just set it up and check it once in a while. It has to be used daily and kept under control to be used effectively

Incentivized

Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices. In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch. We've tried Solr in the past be we encountered issues which were deal-breaking for us. MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.

Incentivized

I would always recommend Splunk over IBM Security QRadar SIEM unless you're trying to save money or only onboarding and normalizing well known data sources. IBM Security QRadar SIEM doesn't seem to handle RBA and complicated, chaining correlation rules very effectively and if I had to write a custom add-on for custom data, I found it easier to do so in Splunk.

Incentivized

• I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.
• One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.
• On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.

Incentivized

• Like any cybersecurity product, any money you don't lose on a prevented attack is money that you saved
• Most of the apps are free and provides great enrichment like User Behaviour Analytics
• Top quality alerts gives enormous value to the "passive" data that flows into the infrastructure

Incentivized