On August 21, 2021, the Cybersecurity and Infrastructure Security Agency (CISA) released an urgent notice related to the exploitation of ProxyShell vulnerabilities ( CVE-2021-31207 , CVE-2021-34473 , CVE-2021-34523 ). By chaining these vulnerabilities together, threat actors are compromising unpatched Microsoft Exchange servers and gaining footholds into enterprise networks.

Since its inception, Elastic Security has had a clear mission: to protect the world's data and systems from attack. We started with SIEM, built on top of the Elastic Stack, applying its fast and scalable search capabilities to detect security vulnerabilities across all threat vectors. Next, we joined forces with Endgame to integrate endpoint security into Elastic Security, and allow customers to prevent, detect, and respond to attacks from a single, unified platform.

As a powerful search engine, Elasticsearch provides various ways to collect and enrich data with threat intel feeds, while the Elastic Security detection engine helps security analysts to detect alerts with threat indicator matching. In this blog post, we’ll provide an introduction to threat intelligence and demonstrate how Elastic Security can help organizations establish robust cyber threat intelligence (CTI) capabilities.

Elastic Security's newest features define the potential of XDR for cybersecurity teams. Our single platform brings together SIEM and endpoint security, allowing users to ingest and retain large volumes of data from diverse sources, store and search data for longer, and augment threat hunting with detections and machine learning. Security vendors are using the term “XDR” with increasing frequency, applying varied definitions to suit their respective technologies.

We are pleased to announce the general availability (GA) of Elastic 7.14, including our Elastic Enterprise Search, Observability, and Security solutions, which are built into the Elastic Stack — Elasticsearch and Kibana. Elastic 7.14 empowers organizations with the first free and open Limitless XDR, which delivers unified SIEM and endpoint security capabilities in one platform.

As we’ve shown in a previous blog, search-based detection rules and Elastic’s machine learning-based anomaly detection can be a powerful way to identify rare and unusual activity in cloud API logs. Now, as of Elastic Security 7.13, we’ve introduced a new set of unsupervised machine learning jobs for network data, and accompanying alert rules, several of which look for geographic anomalies.

The 2010 Stuxnet malicious software attack on a uranium enrichment plant in Iran had all the twists and turns of a spy thriller. The plant was air gapped (not connected to the internet) so it couldn’t be targeted directly by an outsider. Instead, the attackers infected five of the plant’s partner organizations, hoping that an engineer from one of them would unknowingly introduce the malware to the network via a thumb drive.

Detecting and preventing malicious activity such as botnet attacks is a critical area of focus for threat intel analysts, security operators, and threat hunters. Taking up the Mozi botnet as a case study, this blog post demonstrates how to use open source tools, analytical processes, and the Elastic Stack to perform analysis and enrichment of collected data irrespective of the campaign.

I’m a security analyst at Orange Business Services in Paris, and one of my current projects for the Orange Group is implementing a new SIEM based on the Elastic Stack. In this blog post, I’ll share why we chose Elastic and how we were able to integrate Elastic into our existing SIEM, resulting in faster investigations and saving our engineers’ time. So follow along.