Abstract: Embodiments disclosed are directed to a computing system that provides for on-demand scanning for changes in cloud-based object storage systems. The changes can include modifications, additions, or deletions of objects stored in a cloud-based object storage system. The computing system scans, at a first runtime, objects stored in a cloud-based object storage system. The computing system retrieves lookup information associated with a second runtime of a previous scan. Based on the lookup information, the computing system identifies a subset of the objects that were changed or added after the second runtime and before the first runtime. Subsequently, the computing system generates an electronic notification including a consolidated list of the subset of the objects.

Abstract: Embodiments disclosed are directed to a computing system that provides for on-demand scanning for changes in cloud-based object storage systems. The changes can include modifications, additions, or deletions of objects stored in a cloud-based object storage system. The computing system scans, at a first runtime, objects stored in a cloud-based object storage system. The computing system retrieves lookup information associated with a second runtime of a previous scan. Based on the lookup information, the computing system identifies a subset of the objects that were changed or added after the second runtime and before the first runtime. Subsequently, the computing system generates an electronic notification including a consolidated list of the subset of the objects.

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.

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.