Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Today I’m happy to share more about our partnership with Swimlane, which further reinforces our commitment to empowering security teams everywhere. Today’s security teams rely on the power of Elastic’s high-speed, cloud-scale analytics to solve their most complex and pressing security issues. Swimlane’s security automation platform provides a way for these same teams to accelerate and optimize their workflows for max efficiency and to solve SOAR use cases.

Whether this is the third time you are looking at the MITRE Engenuity ATT&CK® evaluation results or your first, you may be asking yourself: what was unique about this year’s evaluation? Well, let’s first start with: who is MITRE Engenuity? They are a tech foundation that collaborates with the private sector on many initiatives — most notably cybersecurity — and in recent years have become synonymous with cyber threat evaluations.

In our previous blog post on Windows access tokens for security practitioners, we covered: Having covered some of the key concepts in Windows security, we will now build on this knowledge and start to look at how attackers can abuse legitimate Windows functionality to move laterally and compromise Active Directory domains. This blog has deliberately attempted to abstract away the workings of specific Windows network authentication protocols (e.g., NTLM and Kerberos) where possible.

TierPoint is a leading provider of secure, connected data center and cloud solutions at the edge of the Internet with thousands of customers. At TierPoint, I’m responsible for maintenance and development of the information security program, which includes threat analytics, incident response, and digital forensics. We’re constantly looking for new and even more effective ways to aggregate, process, and make decisions from massive amounts of data streaming in from diverse sources.

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.

The Elastic Common Schema (ECS) provides an open, consistent model for structuring your data in the Elastic Stack. By normalizing data to a single common model, you can uniformly examine your data using interactive search, visualizations, and automated analysis. Elastic provides hundreds of integrations that are ECS-compliant out of the box, but ECS also allows you to normalize custom data sources. Normalizing a custom source can be an iterative and sometimes time-intensive process.

Cloud API logs are a significant blind spot for many organizations and often factor into large-scale, publicly announced data breaches. They pose several challenges to security teams: For all of these reasons, cloud API logs are resistant to conventional threat detection and hunting techniques.

When it comes to your SIEM, your data is only as useful as your ability to ingest and analyze it. To solve complex security problems, your team ideally needs the ability to comprehensively monitor events within your environment with contextual insights from high-volume data sources.