Predicting operational data of asset(s) operating in operational technology network of industrial control systems

A method comprising:

• obtaining a training variational limit(s) corresponding to a plurality of values of a first operating parameter,
• wherein the first operating parameter is associated with an asset(s) operating in a first operating condition, in an architectural level of an operational technology network, associated with an industrial control system,
• wherein the training variational limit(s) is obtained based on a predefined criteria defined by one of a performant operational state and a non-performant operational state of the asset(s) during the first operating condition,
• and wherein the training variational limit(s) is indicative of a range of values of the first operating parameter of the asset, as the asset(s) operates within the first operating condition;
• and
• training a machine learning model based on values conforming to the training variational limit(s), wherein the machine learning model when trained is to predict an outcome of an operation performed by the asset(s) operating within the operational technology network.

A non-transitory computer-readable medium comprising instructions, the instructions being executable by a processing resource to:

• obtain values of a first operating parameter of an asset(s) operating within a first operating condition, in an architectural level of an operational technology network associated with an industrial control system;
• process the values based on a predefined criteria associated with the asset(s) to determine a variational limit(s) for the first operating parameter, wherein the predefined criteria are to define one of a performant operational state and a non-performant operational state of the asset(s) during the first operating condition, and wherein the variational limit(s) is indicative of a range of values of the first operating parameter of the asset, as the asset(s) operates within the first operating condition;
• and
• transmit the variational limit(s) to a control system hosted in an informational technology network within the industrial control system, wherein the variational limit(s) is utilized by the control system as an input to train a machine learning model, wherein the machine learning model when trained using the variational limit(s) is to predict an outcome of an operation performed by the asset(s) operating within the operational technology network.