In the ever-evolving landscape of cybersecurity, the methodologies and tools we rely on are continually advancing. One of the latest trends is Detection as Code (DaC), which promises to revolutionize threat detection by integrating software engineering practices into security operations. Having worked in an organization that practices DaC for several years, I’ve come to realize that it’s not simply a matter of whether or not to adopt DaC.

In the field of detection engineering, understanding and applying the right frameworks is crucial for effectively identifying and responding to threats. Mental models provide a structured way of categorizing and approaching the myriad of security alerts and anomalies we face daily. These models help us dissect and understand the behavior of potential threats, allowing for a more targeted and effective defense strategy. In this blog post, I’ll explain how I use one mental model to clearly…

Raise your hand if you’ve worked on suppressing a false positive for a detector and then the very next day, it’s back in your “fix” queue with another false positive. I’d expect almost everyone who’s worked in detection engineering for any period of time has their hand raised or is nodding along. Why does this happen? Well, sometimes the detector itself isn’t very good and it’s alerting on a lot of benign behavior. But sometimes, the answer is that…