In an earlier blog post, we spoke about building your own ProblemChild framework from scratch in the Elastic Stack to detect living off the land (LOtL) activity. As promised, we have now also released a fully trained detection model, anomaly detection configurations, and detection rules that you can use to get ProblemChild up and running in your environment in a matter of minutes.
Ten times faster at a fraction of the cost. If you want a headline as to why you should consider adopting Elastic for security and observability, that is it. We often work with our customers to help them establish the business value of Elastic within their organizations. We commissioned Forrester to conduct a Total Economic Impact (TEI) study of our security and observability solutions so our customers have an unbiased view that they can share with their internal stakeholders.
This quick blog is the first in a two-part series discussing a userland Windows exploit initially disclosed by James Forshaw and Alex Ionescu. The exploit enables attackers to perform highly privileged actions that typically require a kernel driver.
In January 2021, we announced that starting with version 7.11, we would be changing the Apache 2.0 portions of Elasticsearch and Kibana source code to be dual licensed under Elastic License and SSPL, at the users’ discretion. As part of that change, we created Elastic License 2.0 (ELv2) as a permissive, fair-code license, which allows free use, redistribution, modification, and derivative works, with only three simple limitations, outlined in our original announcement.
Creating an entity-centric index that contains only the latest document for each entity can be useful in a number of situations. For example, maybe you're managing an ecommerce site and you want to track the latest order placed by each of your customers. Or maybe you want to run a campaign targeting customers who haven't been active over a certain period. What's the fastest and most efficient way to compile and organize such data?
