Implementing a machine-learning model to identify critical systems in an enterprise environment

A system comprising:

• one or more processors;
• and
• one or more computer-readable media, having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• training a machine-learning model, using a training dataset that distinguishes between critical systems and non-critical systems, to classify a particular computer system as critical or non-critical, wherein a label is applied to the particular computer system during the training that identifies the particular computer system as critical or non-critical, and wherein parameters that describe the critical systems or non-critical systems are used as features during the training;
• receiving an input dataset that describes a plurality of computer systems in an enterprise environment;
• outputting, using the trained machine-learning model, an identification of one or more critical systems of the plurality of computer systems within the enterprise environment and an identification of one or more non-critical systems of the plurality of computer systems within the enterprise environment, wherein each identification is associated with a confidence level;
• receiving feedback from a first administrator that includes a reclassification of a non-critical system to a reclassified critical system;
• responsive to receiving the feedback, determining whether the confidence level associated with the reclassified critical system exceeds a threshold level;
• responsive to determining that the confidence level exceeds the threshold level, generating a notification to the first administrator that includes the confidence level and requesting confirmation of the reclassified critical system;
• and
• prioritizing resources by assigning a greater number of administrators to review threats of malicious attacks on the one or more critical systems than a number of administrators assigned to review threats of malicious attacks related to the one or more non-critical systems.

A computer-program product that includes one or more non-transitory computer-readable media with instructions stored thereon that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• training a machine-learning model, using a training dataset that distinguishes between critical systems and non-critical systems, to classify a particular computer system as critical or non-critical, wherein a label is applied to the particular computer system during the training that identifies the particular computer system as critical or non-critical, and wherein parameters that describe the critical systems or non-critical systems are used as features during the training;
• receiving an input dataset that describes a plurality of computer systems in an enterprise environment;
• outputting, using the trained machine-learning model, an identification of one or more critical systems of the plurality of computer systems within the enterprise environment and an identification of one or more non-critical systems of the plurality of computer systems within the enterprise environment, wherein each identification is associated with a confidence level;
• receiving feedback from a first administrator that includes a reclassification of a non-critical system to a reclassified critical system;
• responsive to receiving the feedback, determining whether the confidence level associated with the reclassified critical system exceeds a threshold level;
• responsive to determining that the confidence level exceeds the threshold level, generating a notification to the first administrator that includes the confidence level and requesting confirmation of the reclassified critical system;
• and
• prioritizing resources by assigning a greater number of administrators to review threats of malicious attacks on the one or more critical systems than a number of administrators assigned to review threats of malicious attacks related to the one or more non-critical systems.