During this year’s Black Hat in Las Vegas, I learned (or was reminded of) many lessons working alongside my Corelight colleagues and Black Hat Network Operations Center (NOC) teammates from Arista, Cisco, Lumen, NetWitness and Palo Alto Networks. The uniqueness of standing up a full security stack and NOC in such a short time with a team that comes together infrequently really forced me to consider how team processes and communication affect NOC/SOC efficiency and effectiveness.

The Corelight App for Splunk provides the foundation for organizations to boost SOC effectiveness and productivity by using Corelight data in Splunk. In this blog, I’ll walk through how the Corelight App leverages Splunk’s Common Information Model (CIM) to enhance users' search experience when they are using Corelight data.

In Part 1 of this series, we talked about some challenges with building sufficient coverage for detecting security threats. We also discussed how telemetry sources like logs are invaluable for detecting potential threats to your environment because they provide crucial details about who is accessing service resources, why they are accessing them, and whether any changes have been made.

Over the past few months, ransomware targeting healthcare organizations has been on the rise. While ransomware is nothing new, targeting healthcare organizations, at the extreme, can impact an organization’s ability to engage in anything from routine office visits to life-or-death diagnoses, treatments, and patient care.

In my previous article, I proposed ways that modern network-derived evidence applies to the cyber kill chain—a concept created by Eric Hutchins, Michael Cloppert, and Rohan Amin that changed how security teams approach defending their digital assets. This article focuses on an evolved, non-linear version of the kill chain called the “kill web.”