Elasticsearch vs. LogRhythm NextGen SIEM Platform

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

LogRhythm is good for providing a comprehensive view of the environment. It gives a great outline of whatever is going on in our servers and systems regarding security malfunctions. The SIEM sends real-time notifications when there are some occurrences; like creating a new user and inappropriate login attempts. It also avails a good use case that meets our HIPAA compliance.

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

• LogRhythm is a great SIEM to learn content on because the building blocks are very intuitive and easy to implement. All of the concepts relevant to content development are literally represented as drag and drop building blocks that can be easily manipulated.
• The statistical building blocks contain powerful anomaly detection capabilities that are extremely difficult to implement in other SIEMs or not possible at all.
• LogRhythm does better event classification than any other SIEM by far. My team typically drops all classification schemes from default installations of SIEMs and rebuilds them from scratch. I can actually use LogRhythms event classifications in rules without worrying about excessive partial matches or correlating unwanted events.

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

• While searching for log events is quick, the interface isn't as user-friendly as other SIEM products.
• Many of the administrative/management functions are only available through the full LogRhythm desktop console, not through the web console.
• The LogRhythm agent, when used for FIM and RIM, is very memory intensive.

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

LogRhythm is focused on SIEM. That is their core business. Cost of operations, feature set and ease of use. The Log Rhythm support team is outstanding. Overall reliability is good. Reporting module needs some improvement and LR is promising that there will be significant improvements in future releases.

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

LogRhythm does a rather decent job of making the functionality advanced (allowing for advanced keyword & field searching, use of "AND" as well as "OR" statements in the search bar) while keeping it accessible (by not requiring a specific syntax to do quick searches). This combined with a user interface that has headings and labels that are intuitive is very helpful.

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

Support has always been fantastic for this product compared to many other support providers I've worked with. They are always very friendly and seem to be well trained and knowledgeable and never have to wait long for a solution. We usually get the issue fixed in the first call, but also we really haven't had to use support a ton so that's also a plus

Incentivized

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

Incentivized

• Buy professional services.
• Buy and implement the system if possible.
• Remember that the end point log configuration may require other teams in your company to assist you in getting the desired logs from all resources.
• Attend the end user and daily operations training after a period of usage so you are not overwhelmed with information on concepts not yet seen.
• Don't be afraid to call for help during your first months of use.
• Don't close any ticket until you are sure the expected results are verified.
• Use the community forums to discuss issues with your peers.
• Watch the training videos offered by L R University.

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

The only thing we chose LogRhythm NextGen SIEM Platform for is to allow the Security Analysts to work on the dashboards which don't know much about programming and query languages but has good intuition about cyber-security. It is easy to get hands-on compared to Splunk, which has an initial learning curve before being able to start harnessing its true power. Also, the ticketing system is quite fancy and somehow shows us the recent tickets that we need to jump on, which is not 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

• We were able to retire a few older log collection platforms that we had in house. There were 2-3 systems doing the job of LogRhythm.
• We were able to bring some part of the analysis of events back in house and not rely on third party MSS.

Incentivized