Consolidating detection data is only the first step. In Part 3 of our Detection-as-Code series, we explain how we validate schemas and shared fields, resolve inconsistencies, and integrate checks into GitHub CI pipelines. Tiered validation blocks invalid changes while allowing teams to fix non-urgent issues over time.
Moving to Detection-as-Code starts with making existing detection data reliable and machine-readable. In Part 2, we explain how we validated documents, built deployment tooling, scripted the migration, and used dry runs to verify that generated artefacts matched the existing setup.
How do you introduce Detection-as-Code into an environment where detections are already being developed and deployed? This customer case study examines the technical challenges and migration steps involved in improving detection engineering while keeping existing operations running.
In Part 7, we showcased how we can leverage automation to continuously monitor the performance and trigger rate of our deployed detections. In this part, we are going to investigate how we can introduce automation and utilize continuous deployment pipelines to streamline the tedious task of tuning our detections.
In this part, we are going to introduce automation to effectively monitor our deployed detections. By setting up automations at this phase we adopt a proactive approach towards maintenance, allowing our team to take action before a blowout of alerts or an untuned detection is escalated by the SOC.
The deployment phase is one of the most challenging steps in the Detection Development Life Cycle due to its implementation complexity. In this part, we will explore the principles and practices of deploying rules to target platforms. Additionally, we will go through some of the challenges encountered when designing and implementing a deployment pipeline, along with suggestions on how to overcome them, to ensure that our Continuous Deployment pipeline operates smoothly.
