This report explains how Self-Supervised Learning removes the main technical choke point in defense AI: the need for labeled data. The DoD collects enormous volumes of ISR, radar, and SIGINT feeds that mostly sit unused because supervised methods can’t train on them without human annotation. SSL eliminates that bottleneck. It lets models learn directly from raw sensor data inside secure, air-gapped facilities, turning years of stored collection into usable training fuel. The result is sovereign models that stay on classified networks and can be repurposed for new missions in days instead of months.

SSL replaces dependence on commercial, internet-trained AI with defense-built systems trained on DoD sensors, using DoD compute, under DoD control. The report spells out how to implement it: require SSL-ready data architectures in OEM contracts, build dedicated pretraining enclaves, and fund continual retraining like any other sustainment activity.

Download the full Deca Report below.