In reservoir computing, a readout layer is the trained output stage that converts a reservoir’s changing internal states into a result, such as a classification or prediction. The reservoir transforms incoming data through its dynamics; typically, only the readout is trained, often using a relatively simple model.
This division can make reservoir computing easier to train, but the readout’s quality depends on the states it receives and on how well the training data represents real inputs. In security applications, a readout might classify network activity or sensor signals. Defenders should validate it against varied and adversarial inputs, since misleading inputs or distribution shifts can cause incorrect outputs. The term describes a machine-learning component, not a security control by itself.