Distillation is a separation process that uses differences in boiling points to split a mixture into parts. The liquid is heated, vaporized, and then condensed so that components can be collected separately. In chemistry, this is useful when one substance evaporates more easily than another.
In cyber security, distillation is usually a metaphor, but the idea still matters: defenders often separate high-value signals from noisy data, such as logs, alerts, or packet captures. In machine learning security, “model distillation” is a real technique for compressing a large model into a smaller one that is faster to run on security tools. Attackers can also benefit from careful separation of data, for example when sorting stolen files or filtering credentials from large dumps. The core lesson is the same: isolate what behaves differently so it can be handled more effectively.