Custom patching automation with machine learning integration

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising one or more processors, memory, and a communication interface, cause the computing platform to:

• identify, based on a knowledge base of a machine learning module and analysis of a plurality of servers, a vulnerability associated with a server of the plurality of servers;
• schedule, based on analysis of system information associated with the server and analysis of the knowledge base by the machine learning module, a time interval associated with the server, wherein the time interval comprises an outage period for the server and a tracking module processes information for time intervals associated with the plurality of servers;
• generate, automatically and by a patch generator, a patch job comprising an update to software running on the server to correct an identified vulnerability, wherein the patch job specifies that sub-processes of the patch occur in a specific sequence;
• deploy, to the server, the patch job;
• validate, during the time interval and continuously during execution of the patch job, that the sub-processes are ordered correctly;
• modify, based on an indication that the sub-processes are ordered incorrectly, an update sequence of the patch job;
• cause display, via a network and at a user interface of a computing device, updates comprising information associated with execution of a patch job;
• analyze, by the machine learning module, an assessment report associated with an executed patch job;
• and
• store, at the knowledge base of the machine learning module, results of the analysis of the assessment report associated with the executed patch job.

A method, comprising:

• identifying, based on a knowledge base of a machine learning module and analysis of a plurality of servers, a vulnerability associated with a server of the plurality of servers;
• scheduling, based on analysis of system information associated with the server and analysis of the knowledge base by the machine learning module, a time interval associated with the server, wherein the time interval comprises an outage period for the server and a tracking module identifies information for existing time intervals associated with the plurality of servers;
• generating, automatically and by a patch generator, a patch job comprising an update to software running on the server to correct an identified vulnerability, wherein the patch job specifies that sub-processes of the patch occur in a specific sequence;
• causing execution, after deployment to the server and based on the time interval, of the patch job associated with the vulnerability at the server;
• validating, continuously during execution of the patch job, that the sub-processes are ordered correctly;
• modifying, based on an indication that the sub-processes are ordered incorrectly, an update sequence of the patch job;
• causing display, via a network and at a user interface, updates comprising information associated with execution of the patch job;
• analyzing, by the machine learning module, an assessment report associated with an executed patch job;
• and
• updating the knowledge base of the machine learning module based on the analysis of the assessment report associated with the executed patch job.