Abstract: Aspects described herein may allow keystroke logs to be monitored. A computing device may receive a plurality of keystroke logs and provide, to a machine learning model, the plurality of keystroke logs. The computing device may receive, as output from the machine learning model, a value representing a likelihood that a first keystroke log comprises a first command to change a computing system. The computing device may retrieve, from a database, one or more change orders, each indicating an authorization to change the computing system. The computing device may send, to a second computing device and based on determining that the first command does not match the one or more change orders, an alert indicating the first keystroke log. In this way, unauthorized change to the computing system may be detected.

Abstract: Aspects described herein allow for systems and methods to monitor production changes to resources in a cloud computing environment and display the change activity via a change event dashboard. A change order monitoring application receives data from cloud computing audit logs to detect infrastructure changes and combines that data with application information to determine which application was affected. The change order monitoring application then uses a machine learning algorithm to cluster multiple change events together when it is likely that the change events were part of the same change. The change event dashboard visually displays real-time cloud computing application changes.

Abstract: Aspects described herein allow for systems and methods to monitor production changes to resources in a cloud computing environment and determine whether those changes were performed in accordance with a change management policy. A change order monitoring application receives data from cloud computing audit logs to detect infrastructure changes and combines that data with application information to determine which application was affected. The change order monitoring application then uses a machine learning algorithm to cluster multiple change events together when it is likely that the change events were part of the same change. If cluster of change activity does not appear to be authorized, the change order monitoring application sends an alert to a change management team and an application team to get more information about the activity.

Abstract: Aspects described herein allow for systems and methods to monitor production changes to resources in a cloud computing environment and determine whether those changes were performed in accordance with a change management policy. A change order monitoring application receives data from cloud computing audit logs to detect infrastructure changes and combines that data with application information to determine which application was affected. The change order monitoring application then uses a machine learning algorithm to cluster multiple change events together when it is likely that the change events were part of the same change. If cluster of change activity does not appear to be authorized, the change order monitoring application sends an alert to a change management team and an application team to get more information about the activity.