The ability for security teams to integrate threat data into their operations substantially helps their organization identify potentially malicious endpoint and network events using indicators identified by other threat research teams. In this blog, we’ll cover how to ingest threat data with the Threat Intel Filebeat module. In future blog posts, we'll cover enriching threat data with the Threat ECS fieldset and operationalizing threat data with Elastic Security.

At Elastic, we internally use, test, and provide feedback on all of our products. For example, the Information Security team is helping the Product team build a stronger solution for our customers. The InfoSec team is an extremely valuable resource who acts not only as an extension of Quality Assurance/Testing, but also as a data custodian.

It’s no secret that organisations are up against skilled, relentless and determined adversaries. Security operations teams need to continuously test their detection capabilities by carrying out adversary emulation plans that are made up of varying tactics, techniques and procedures (TTPs) and track key metrics of their coverage in order to close any existing gaps. There are many tools available for running adversary emulation plans and performing purple team exercises.

The Splunk Attack Range project has officially reached the v1.0 release. By achieving this milestone, we wanted to reflect on how we got here, what features we’ve built for v1.0 and what the future looks like for Splunk Attack Range. What is the Splunk Attack Range? 🧐

Implementing effective threat detection for AWS requires visibility into all of your cloud services and containers. An application is composed of a number of elements: hosts, virtual machines, containers, clusters, stored information, and input/output data streams. When you add configuration and user management to the mix, it’s clear that there is a lot to secure!

At Elastic Security, we approach the challenge of threat detection with various methods. Traditionally, we have focused on machine learning models and behaviors. These two methods are powerful because they can detect never-before-seen malware. Historically, we’ve felt that signatures are too easily evaded, but we also recognize that ease of evasion is only one of many factors to consider.