Security policy selection based on calculated uncertainty and predicted resource consumption

A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• receive, by the computer, a request to perform a security policy implementation analysis for a first deployment associated with a first client in an IT environment;
• collect, by the computer, IT information associated with the first deployment;
• apply, by the computer, trained machine learning models to analyze the IT information of the first client to compute a security policy for the first deployment,
• wherein the security policy is computed based on a calculated uncertainty of effects that applying the security policy to the first deployment is capable of causing, and a predicted amount of resources of the first deployment that applying the security policy to the first deployment would consume;
• output, by the computer, an indication of the security policy for display in a dashboard on a display of a user device of the first client;
• to train, by the computer, the machine learning models to analyze IT information;
• and
• store, by the computer, the trained machine learning models to a predetermined database, wherein training the machine learning models includes:
• retrieving IT information associated with a second deployment of a training IT environment;
• computing risk level errors from components of the second deployment and applications of the second deployment;
• transforming the IT information into training datasets for the machine learning models;
• training a first of the machine learning models using a first of the training datasets, wherein the first of the machine learning models is trained for calculating uncertainty;
• and training a second of the machine learning models using a second of the training datasets, wherein the second of the machine learning models is trained for predicting resource consumption.

A system, comprising:

• a hardware processor;
• and
• logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
• receive a request to perform a security policy implementation analysis for a first deployment associated with a first client in an IT environment;
• collect IT information associated with the first deployment;
• apply trained machine learning models to analyze the IT information of the first client to compute a security policy for the first deployment,
• wherein the security policy is computed based on a calculated uncertainty of effects that applying the security policy to the first deployment is capable of causing, and a predicted amount of resources of the first deployment that applying the security policy to the first deployment would consume;
• output an indication of the security policy for display in a dashboard on a display of a user device of the first client;
• and
• the logic being further configured to train the machine learning models to analyze IT information;
• and store the trained machine learning models to a predetermined database, wherein training the machine learning models includes:
• retrieving IT information associated with a second deployment of a training IT environment;
• computing risk level errors from components of the second deployment and applications of the second deployment;
• transforming the IT information into training datasets for the machine learning models;
• training a first of the machine learning models using a first of the training datasets, wherein the first of the machine learning models is trained for calculating uncertainty;
• and training a second of the machine learning models using a second of the training datasets, wherein the second of the machine learning models is trained for predicting resource consumption.