The logs were flooding in—terabytes of events per day—and every second lost meant evidence gone forever. Manual parsing was too slow. Heavy AI pipelines demanded GPUs you didn’t have. The answer was clear: an evidence collection automation lightweight AI model running CPU only, built to move at the speed of an incident.
A lightweight AI model for evidence automation strips complexity to the bone. It minimizes parameters, cuts memory footprint, and optimizes inference paths to run in real time on commodity hardware. CPU-only operation means deployment without provisioning GPUs or specialized accelerators. You get consistent performance across environments—on-prem, cloud, or edge—without dependency nightmares.
The core design starts with sub-linear feature extraction tuned for high-volume logs, network captures, and system traces. Models can be trained on curated datasets of threat signatures, anomaly patterns, and forensic artifacts. Quantization pushes size down while preserving precision critical for evidentiary integrity. No wasted cycles; every operation moves toward immediate classification and correlation.