Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial " perturbations " —small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively " smooths " the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms. Defensive distillation specifically targets the model ' s resilience to technical exploitation.