A two-step process where a compact student network is trained to mimic the output distributions (soft labels) or intermediate representations of a larger, more complex teacher model. Distillation reduces inference latency, energy use, and attack surface - important for edge deployment or regulated environments. Governance includes validating that distilled models maintain fairness and accuracy, and documenting the distillation recipe for audit and reproducibility purposes.