The LogRhythm NextGen SIEM Platform, from LogRhythm in Boulder, Colorado, is security information and event management (SIEM) software which includes SOAR functionality via SmartResponse Automation Plugins (a RespondX feature), the DetectX security analytics module, and AnalytiX as a log management solution that centralizes log data, enriches it with contextual details and applies a consistent schema across all data types.
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.
Perfect for projects where Elasticsearch makes sense: if you decide to employ ES in a project, then you will almost inevitably use LogStash, and you should anyways. Such projects would include: 1. Data Science (reading, recording or measure web-based Analytics, Metrics) 2. Web Scraping (which was one of our earlier projects involving LogStash) 3. Syslog-ng Management: While I did point out that it can be a bit of an electric boo-ga-loo in finding an errant configuration item, it is still worth it to implement Syslog-ng management via LogStash: being able to fine-tune your log messages and then pipe them to other sources, depending on the data being read in, is incredibly powerful, and I would say is exemplar of what modern Computer Science looks like: Less Specialization in mathematics, and more specialization in storing and recording data (i.e. Less Engineering, and more Design).
LogRhythm NextGen SIEM Platform has an alarm system that generates tickets based on the event and the way it has been configured in the LogRhythm console. Let's say we have a ticket for a malicious email attachment. The ticket will some information like the source of the log, the source IP, destination IP etc. It can be drilled down to obtain specific information like the recipient, source location, file attachment name, SHA hash of the file, source and destination port, time, mac address of the machine that downloaded it etc. This helps the analysts to go to the root of the cause and take actions easily without manually parsing them.
The second good thing about the LogRhythm NextGen SIEM Platform is that it is very easy to use with its well-structured interface. To use LogRhythm, an user barely require any technical skills. A little overview of IP, CIDR, hash, etc. is enough to get your hands on it. It requires no programming or coding skills, as everything is GUI based. It also provides a beautiful visualization dashboard. There is another beautiful feature that it provides for the classification of events, known as cases. Multiple users working on the same platform can create cases and add events to it. They also help to maintain future reference.
The third good feature is the search tool which is very powerful. For example, sometimes it is hard to find the users who downloaded a malware from the guest wireless of the institution and not the private network. The search tool helps us in searching the user by automatically correlating the MAC address from the current network logs and the previous logs as the MAC address is the same. It is highly scalable for parsing a large number of logs from various sources.
I particularly think this is one of the best software available for log parsing in an organization where non-technical users are working on incident response. This tool has a good amount of flexibility. However, it can only be configured with the LogRhythm NextGen SIEM Platform Console.
In terms of usability, as already mentioned, it is a very easy tool to use, with a GUI based interface.
Logstash design is definitely perfect for the use case of ELK. Logstash has "drivers" using which it can inject from virtually any source. This takes the headache from source to implement those "drivers" to store data to ES.
Logstash is fast, very fast. As per my observance, you don't need more than 1 or 2 servers for even big size projects.
Data in different shape, size, and formats? No worries, Logstash can handle it. It lets you write simple rules to programmatically take decisions real-time on data.
You can change your data on the fly! This is the CORE power of Logstash. The concept is similar to Kafka streams, the difference being the source and destination are application and ES respectively.
LogRhythm absolutely needs to provide back end support for threat intelligence lists. Performing a linear search on massive lists of IPs on incoming web traffic can bring the SIEM to its knees.
LogRhythm should drop its entire code base for implementing lists and simply turn them into hash tables to avoid the excessive cost associated with referencing lists in rules. I haven't seen the code, but the performance suggests O(n).
The reporting feature is the worst of all SIEMs, luckily reports are not my primary service offering. LogRhythm should definitely revamp its reporting to be more intuitive.
Since it's a Java product, JVM tuning must be done for handling high-load.
The persistent queue feature is nice, but I feel like most companies would want to use Kafka as a general storage location for persistent messages for all consumers to use. Using some pipeline of "Kafka input -> filter plugins -> Kafka output" seems like a good solution for data enrichment without needing to maintain a custom Kafka consumer to accomplish a similar feature.
I would like to see more documentation around creating a distributed Logstash cluster because I imagine for high ingestion use cases, that would be necessary.
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.
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.
While LogRhythm support is generally quick to respond, the initial response is usually from a first line support engineer with general knowledge of the product. Any advanced or complex issues have always required the assistance of a higher tier of support, directly or indirectly. For a few occasions we actually used our PS hours to work on the issue.
LogRhythm was simpler to set up and configure as well as extract information from. It also was less intrusive in terms of how many appliances were needed to implement. We were up and running within 5 hours to start accepting log sources. We selected LogRhythm as well since support is based in the USA in Colorado.
MongoDB and Azure SQL Database are just that: Databases, and they allow you to pipe data into a database, which means that alot of the log filtering becomes a simple exercise of querying information from a DBMS. However, LogStash was chosen for it's ease of integration into our choice of using ELK Elasticsearch is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational
The ability to search through logs in a centralized location really helps us to provide RCA (Root Cause Analysis) to management for outages. This helps us to quickly identify the cause of outages and thus saves money due to reduced downtime.
Being able to configure the alarms to provide real-time notification (and responses) to security events helps to prevent potential loss due to compromises (such as a fraudulent wire transfer).
The initial investment in LogRhythm SIEM is somewhat expensive, however, the appliance is built to your specific needs so you won't have to constantly be upgrading the device as your company grows.
Positive: Learning curve was relatively easy for our team. We were up and running within a sprint.
Positive: Managing Logstash has generally been easy. We configure it, and usually, don't have to worry about misbehavior.
Negative: Updating/Rehydrating Logstash servers have been little challenging. We sometimes even loose data while Logstash is down. It requires more in-depth research and experiments to figure the fine-grained details.
Negative: This is now one more application/skill/server to manage. Like any other servers, it requires proper grooming or else you will get in trouble. This is also a single point of failure which can have the ability to make other servers useless if it is not running.