providing a digital key that is associated with a particular entity, wherein the particular entity has access to a trained machine learning model that is trained to generate one or more outputs based on data applied across a plurality of inputs; causing the digital key to be applied as input across at least a first portion of the trained machine learning model to transition one or more gate nodes that are interspersed within the trained machine learning model between a locked and an unlocked state; and

causing other input data to be applied as input across at least a second portion of the trained machine learning model to generate one or more of the outputs;

wherein the one or more gate nodes include a gated layer of gate nodes located between two layers of the trained machine learning model having at least three layers including an input layer, one or more hidden layers, and an output layer,

wherein the gated layer is configured to selectively control flow of processed data to and from the one or more hidden layers located between the input layer and the output layer of the trained machine learning model,

wherein in the unlocked state, a given gate node of the one or more gate nodes allows data received from an upstream layer of the trained machine learning model to pass unaltered to a downstream layer of the trained machine learning model; and model,

wherein in the locked state, the given gate node does not allow the data received from the upstream layer of the trained machine learning model to pass unaltered to the downstream layer of the trained machine learning model,

wherein the one or more gate nodes of the gated layer is coupled to a node of the one or more hidden layers having a higher influence on the one or more outputs than other nodes of the one or more hidden layers, and

wherein the gated layer is customizable for individual authorized users and operated by digital keys that indicate a level of access of the individual authorized users.