Data Science for Detection Engineering Maintenance

FalconForce shows how to stop hand-tending an ever-growing detection flock by systematically collecting detection code, environment metadata, and post-deployment metrics. It is the second part of a series whose first post argued that maintenance is the stage of the detection lifecycle nobody talks about, and sorted it into corrective, adaptive, perfective and preventive work. The collected numbers surface what needs attention: possibly broken detections that have been silent for six-plus months, noisy ones consistently firing more than once a day, and deprecation candidates. Maintenance decisions are driven by time-series alert patterns and metadata rather than SOC ticket classifications. Which, as anyone who has audited a ticket queue knows, is exactly the right amount of trust to place in SOC ticket classifications.