As a detection engineer, container escapes have always fascinated me. Detections focused on control bypasses can be both extremely difficult to make and extremely valuable when they fire. The challenge is that most discussions about container runtime security focus on whether a tool detected an escape – a binary yes/no that doesn’t tell you much about the underlying telemetry that makes detection possible in the first place.

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…