Elastic Security Reviews

My use case for the product revolved around conducting demonstrations and testing. It also helped me with tracing ransomware and managing threat scenarios.

The integration with Siemens Endpoint Security in Elastic Security has been beneficial for security. The provided rules are good, making it easy to create and understand rules. Patterns and detections are made through index patterns, requiring some follow-up steps.

In real-time, the impact of Elastic Security on ransomware is significant. For known and repeated ransomware, it can detect and prevent effectively using established signatures and behavioral patterns. However, for new types of ransomware with less complex behaviors or those that modify files minimally, conventional detection methods may struggle. Elastic Security proves to be effective even in challenging cases.

On the cloud, it allows testing of SaaS-based applications, performance evaluations using CDMs and APIs, incident detection within company network infrastructures, and comprehensive management of security services.

Improvements in Elastic Security could include refining and normalizing queries to make them more user-friendly, enhancing the user experience with better documentation, and addressing any latency issues.

I have utilized Elastic Security for approximately three to four months.

I rate the product’s stability an eight out of ten.

Scaling Elastic Security is relatively easy, with a rating of seven out of ten.

The tool's deployment is straightforward. 

I rate the overall product an eight out of ten.

Elastic Security is very easy to adapt. 

The tool should improve its scalability. 

I have been working with the product for seven years. 

The solution is stable. 

Our DevOps uses the product regularly. 

I would rate the solution a seven out of ten. 

The product has huge integration varieties available. 

The tool needs to integrate with legacy servers. Big companies can have legacy servers that may not always be updated. 

I have been working with the solution for the last eight months. 

The solution is scalable and flexible. My company has 20 users for the product. 

We had relied on in-house support initially. However, we understand now that there are a few areas where we need to have vendor support. So we have contacted a few different companies and contractors for it. In the beginning, it may be possible to do support in-house. However, if you have a lot of commercial production environment services, then it is very hard to do without vendor support. 

We decided to use the solution because it was a very promising tool and other alternatives had limitations. The tool has availability, data infrastructure, data uptime, etc. The solution is quite flexible in terms of cost. You don't need to buy a license for each and everything. Whenever you require a license, you can just buy it. I think these are the two main drivers. The product is quite open in terms of integration with machine learning which helps us with proactive monitoring. 

The product's initial setup is very easy. I think the most important point is how you design your infrastructure because the solution is quite open. So you have to design it based on the nature of the data. You also need to get a life cycle so that there is no load on the storage. The solution's flexibility depends on how you design it. 

The tool's pricing is flexible and comes at unit cost. You don't have to pay for everything. 

I would rate the product an eight out of ten. You should use the solution if you want to have a very detailed machine-learning artificial intelligence. However, for certain production licenses, you need to prepare. It is open to different configurations and can just fit according to your requirements. This is one of the solution's good parts. 

We are using Elastic Security for logging the application logs, as we use a microservice architecture. So all application logs are saved to this LogSpot.

It helps us detect errors and keep an eye on the application in both the development and production environments.

It can handle millions of loads at a time, and you can always use the filters to find exactly what you are looking for and detect errors in every log message you are searching for, basically.

There is an area of improvement in the Logs list. The load list may need to be paginated as there are limits. So if you are looking for logs for a specific application, you may get 50 lines of logs, but then you are lost. You need to add more features to specify your request so you can get the final result. It would be better to have additional features to specify your request and get the complete result.

I have been using this solution for nine months. Although, I am not using the latest version. 

I would rate the stability a nine out of ten. 

I would rate the scalability an eight out of ten. 

We definitely saw an ROI. It quickly finds the bugs.

I would recommend using it, especially if you have a microservice architecture. I also have a friend who has been using it for some big data projects, so I would recommend it for that as well. 

Overall, I would rate the solution a nine out of ten. 

Overall, the solution is good.

The machine learning aspect of the solution has been great.

The deployment is not that complicated.

ELK is open-source, and it will give you the framework you need to build everything from scratch.

The SIEM modules in Logstash, need more improvement. In the future, the modules could be more advanced as right now there are only very basic modules.

We are facing an issue with the engineers. In the region, there are not many available. Only a few people might be available in our particular region, which is a problem. 

There isn't really a very good user experience. You need a lot of training. There is an interface with limited options. You need to work with the coding and you need to work with the scripts. It's not simple. If you want to configure something, for example, you need to be a proper programmer.

It would ideal if they had a dashboard. Right now, the only way to see what you need to see is to go through all of the logs. 

I've used the solution for one and a half years.

The stability of the solution is good. However, it depends on the configurations. If the solution is configured properly from the beginning, it will be stable. However, if the solution is not configured from beginning properly, it will not be. This is due to the fact that ELK Elasticsearch gives you the framework only, and the customizations depend on the guys who will be coming to configure everything for the company.

The scalability is good, however, there is a certain level of skill that is needed. Due to the lack of trained engineers in the area, this could be a challenge.

We've reached out to technical support in the past. We found that sometimes communication with them was difficult as there was a lack of understanding. This means that it takes a longer time to reach a resolution. However, in the end, when we have had issues, we were able to resolve them, even if it was a bit delayed. 

I've also worked with LogRhythm and there is no comparison. LogRhythm is the best solution for me. The use cases are better and are readily available. In contrast, with ELK, we need to deploy a lot of things. We need to program people and we need skills and training. We need a lot of things. Even the LogRhythm training is easier than ELK. With ELK, you need to build the customization, rules, everything, from scratch. WithLogRhythm, you just have to enable features.

If a company wants some more specific detailed use cases, then ELK would be better than LogRhythm, however, for a generic use case, LogRhythm is better.

The initial setup is pretty simple and straightforward. It's not overly complex. 

That said, it does require trained specialists, and there just aren't that many in our area. 

Overall, I would rate the setup process at a two out of five. 

The configuration must be done correctly, and that depends on who is configuring it. If the person configuring it, for example, only has an administrator background, he will configure the administrator stuff. If he has a security background, he will configure for security.

We are a partner. 

I'd advise others considering the solution that ELK is a good solution, however, it requires skills and capability. You need to be properly trained with it to get the most out of it. 

I would rate the solution at a five out of ten.

We used this solution for gathering our application logs and analyzing application behavior.

This solution assists in tuning our applications.

This is one of the best open-source log management and log analyzer tools in the world.

The documentation for this solution is very important, and more needs to be developed. It was not as good as we expected, and because of that, we prefer to work on commercial solutions such as Splunk or ArcSight. If the documentation were improved and made more clear for beginners, or even professionals, then we would be more attracted to this solution.

As you gather more and more data, and the data continues to grow, I think it is difficult to handle, administer, and perform declustering.

I would like to see support for machine learning, where it can make predictions based on the data that it has learned from our environment.

In terms of stability, we have had many problems when dealing with big data.

There are six people who use this solution in our company.

I do not use the commercial version so I cannot comment on technical support. The open-source community is very important for this solution.

We used Splunk in parallel with this solution.

In my role as a Security Operations Center Analyst, I think that Splunk is more useful for me. This is because I do not work on analyzing application behavior. However, I help my colleagues with this task, using ELK Logstash, based on my experience with Splunk.

The initial setup of this solution was complex.

We have an enterprise structure and we cannot just install this solution, Logstash, and Kibana (the data visualization plugin for this solution), to have a good experience. For example, we had to set up the SQL database.

We now have nine Elasticsearch nodes in the company that all work together in a cluster. It is not simple, but rather, an enterprise structure.

We use the open-source version, so there is no charge for this solution.

The solution does not work as well as Splunk.

Our company uses Logstash for gathering the data, and Kibana for searching. The two are used together.

This is a solution that I recommend. It is the best open-source product for people working in SO, managing and analyzing logs.

I would rate this solution an eight out of ten.

I worked for a telco client for the security model of Elastic, but my role was unit manager. I don't have a lot of technical expertise, but I decided on the solution for a client, and I was responsible for the delivery.

I worked with the security of the mobile app. I see all the logs in Elastic for SIEM. I monitored the logging and some logs from the machine for a UNIX system with some use cases like the machine's file system.

This solution is deployed on-premise.

We provide this solution to our customers, which are telcos, in the finance industry, and in retail.

I think that it's a good solution for a SIEM.

Elastic doesn't have the features like other competitors in SIEM. For example, Dynatrace as a solution for SIEM has features that Elastic actually don't have.

With Elastic, you have to build the use cases for the specific requirement. Other products have a simple integration and more use cases to integrate out-of-the-box solutions for SIEM. That's the improvement I would like to see.

The product is stable.

Other products like Splunk are better than Elastic for a SIEM because there are some use cases already available for a client. Elastic doesn't have this, so the user must build the SIEM solution. I think that Elastic has to increase the features for the SIEM.

It's not very complicated to install Elastic, but I didn't deploy it.

I would rate this solution 7 out of 10.

It's a good solution and I would recommend it, but there are other products that have more features that Elastic doesn't have.

We do not use monitoring due to the fact that we use Prometheus for monitoring. We don't use APM and so on. We use ELK only for logging.

The solution has very good logging functionality. 

The aggregation capability is quite useful. 

The solution is quite stable. The performance has been good.

The solution scales well.

The solution has gotten easier to deploy since the 2019 version.

Using ELK the first time there was a lack of security. We had to buy the paid version due to the fact that we needed to secure access to Kubernetes.

The problem with ELK is it's difficult to administer. When you have a problem, it can be very, very difficult to rebuild indexes. In fact, you have to monitor the stack and it's very, very difficult. Sometimes we lose indexes or we have nothing on the dashboard.

I've been using the solution for about two years at this point. It hasn't been an extremely long amount of time.

The solution is stable. It's reliable. There are no bugs or glitches. It doesn't crash or freeze.

The solution can scale. If a company needs to expand it, it can do so pretty easily.

We use the solution for quite a small team. Ten people work on it.

Due to the fact that we have a paid version of the product, technical support has been fine. We've been satisfied with the level of service provided to us. They are quite helpful and responsive.

Previously, we were on Datadog, Kubernetes Logs. It was not very easy to debug incidents and so on. If I had to compare, I'd say that Datadog is very easy to implement and it's such a fast solution.

The first time, it was very hard to deploy on Kubernetes. However, as we reached version seven, they are now an operator. Now it's very easy to deploy. We no longer have any issues.

The solution is a bit expensive. I don't know the pricing of Datadog, which is what we used to use, however, it's my understanding that it is very expensive also. 

We are a customer and an end-user. We do not have a business relationship with ELK.

The solution is deployed on Kubernetes in Azure.

I would advise other companies and users not to mix monitoring and logging. It's not the same purpose. Many people do monitoring by scanning logs. It's not a good idea. The good idea is to monitor separately. In case of incidents, you have to monitor metrics and logins for the root cause. It's important to separate this, and not treat them as the same thing.

I'd rate the solution at an eight out of ten.