Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
Microsoft Sentinel
Score 8.6 out of 10
N/A
Microsoft Sentinel (formerly Azure Sentinel) is designed as a birds-eye view across the enterprise. It is presented as a security information and event management (SIEM) solution for proactive threat detection, investigation, and response.
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.
We use it because when a user sees the suspicious activity on his account, Microsoft Sentinel gives alerts to the user's system and the admin system as well. When a user of one of our systems clicked a spam email, that email was trying to install a virus on our server, but Microsoft Sentinel gave an alert to the user and admin both, so that is why our team was able to fix that issue with Microsoft Sentinel very fast. However, it will not be the best option for you if your team is utilizing every feature but you are on a tight budget.
It is a good tool for threat detection and analysis of the threats. We are using this tool for real time threat detection on our employee machines as well as some servers.
It provides various options for collecting data sources by leveraging multiple sources using data connectors. This helps us in gathering data from multiple sources such as our servers as well as our employee machines.
One good thing about this tool is automated incident response thereby increasing the security of servers.
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.
It takes some time to learn how to use and install it properly, and it does not connect effectively with external PaaS systems such as Salesforce CRM, Salesforce Commerce Cloud, and so on.
Microsoft can simplify the display of the logs to make them easier to study, and the user interface occasionally delays, which can also be enhanced.
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.
The Microsoft Azure Sentinel solution is very good and even better if you use Azure. It's easy to implement and learn how to use the tool with an intuitive and simple interface. New updates are happening to always bring new news and improve the experience and usability. The solution brings reliability as it is from a very reliable manufacturer.
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.
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.
Microsoft Sentinel excels in cloud-native scalability, Microsoft ecosystem integration, and AI-driven threat detection with UEBA and Fusion rules, offering faster deployment and lower costs (48% cheaper per Forrester) than Splunk, QRadar, Exabeam, SentinelOne, Securonix, and Wazuh. It lags in third-party integrations and syslog parsing. Organizations choose Microsoft Sentinel for its cost-effectiveness, automation, and Microsoft synergy, especially in Azure-heavy environments, though Splunk and Exabeam lead in flexibility and UEBA, respectively.
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.
As any cybersecurity product, this has to be more with risk to avoid loss in case of a ransomware that more than relate to a productivity increase. Maybe the impact could be that instead of having people that are checking 24/7 the dashboard, you could implement Sentinel and have less people checking that or people with less expertise. So the saving will be a minor but will be a saving in the cost of your team.
