Alert Logic delivers managed detection and response (MDR) with comprehensive coverage for public clouds, SaaS, on-premises, and hybrid environments. Alert Logic is a HelpSystems brand since the 2022 acquisition announced in March.
This is pretty good AV product - lightweight, easy to install, and easy on system resources. It will take some getting used to on the end user side, it doesn't scan in a traditional way, and it does not have a taskbar icon so it hard to know if it's working or installed. My only complaint would be the false positives which I know every AV system has, but the problem with Barkly is that it alerts the users with a message (which they freak out about) and it alerts IT with an email. Again, not a major issue, but it can be annoying until it is overridden. The override process is super easy though, so its again, not a big deal.
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).
Customer Service. Usually, I'd put the technical details up front, and they're good with that too. But the service from pre-sales all the way through onboarding and continued account management is top tier. Our onboarding schedule got messed up, partly because of us, but that was rather minor. I always get prompt replies to any tickets, and they've even reached out to discuss my feature requests. When it comes to security, it's critical to have a responsive team, and they've got it.
Detection seems good. It's hard to quantify exactly, but it seems that they always detect the bad actors. And when we get an alert, they include a bunch of details so we know what kind of scan they're trying to do, how far they got, etc. You can't prevent everyone from doing a scan on your IP, but it gives you a really good idea of where your soft spots might be. And if you're getting those low-level alerts, it's a reminder that it's there and working if you have a major event too.
Very easy setup. This goes back to their customer support to some extent, as they walk you through all the steps required. But it's also about their technical solution, it's not so overly complex that it's fragile, nor does it take a great deal of time to deploy. And it's been zero effort to maintain since then.
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.
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.
I was using Alert Logic Insight for myself to improve my skills and ability to it. My organization was not happy using our previous website security program so I recommended for them to use this software. It has been more than 1 year and still, they are using this program without having any problem so far.
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
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.
