Synthetic Data for Defense AI Training: How to Build Training Data Where Real-World Data Is Scarce

South Korea is expanding defense AI infrastructure through GPU investments and plans for an integrated data center, but effective model development remains constrained by security rules, limited access to rare or sensitive targets, and the high cost of collecting and labeling real-world imagery. Synthetic data can supplement these gaps through compositing objects into real images, generative augmentation of conditions such as weather and lighting, and simulator-based generation of labeled 3D scenes. Superb AI describes using these approaches for defense and security datasets, including a simulator pipeline built from human-action samples, indoor environments, and object assets that can produce extensive variations. The approach is most applicable to rare events, unphotographable targets, and costly large-scale projects, while tasks with sufficient real-world data may not require it. Synthetic data should be curated carefully, combined with real data for training, and evaluated against real-world datasets to address the Sim-to-Real gap, with closed-network, on-premises deployment presented as a way to meet defense security requirements.

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