AI-Driven Detection Test Automation

As detections transitioned to code, validating their correctness at scale became a bottleneck. Manually authoring test cases for each detection was time-consuming and inconsistent, while traditional static validation failed to capture behavioural edge cases inherent to security logic.

Designed and implemented an AI-assisted testing system that generated detection test cases automatically as part of the CI pipeline. Engineered strict, deterministic prompts to extract structured test scenarios from detection definitions, ensuring standardised output suitable for automated execution. Integrated these tests into CI workflows, enabling consistent validation of detection logic without increasing manual engineering overhead.

Enabled scalable, repeatable testing of detection logic without requiring bespoke test authoring for each rule. Improved confidence in detection changes by catching logical errors and regressions earlier in the development lifecycle. Demonstrated practical application of AI engineering techniques in production systems with strong constraints on output reliability.