Abstract: In an approach to managing access changes to enterprise resources, one or more computer processors detect a status change of an employment of a user in an enterprise. One or more computer processors retrieve an access level of the user associated with one or more enterprise resources. Based on the status change, one or more computer processors determine the access level of the user associated with at least one of the one or more enterprise resources needs to change. One or more computer processors determine whether the user is an owner of the at least one resource. In response to determining the user is an owner of the at least one resource, one or more computer processors retrieve an employee hierarchy. Based on the employee hierarchy, one or more computer processors assign temporary ownership of the at least one resource to a first employee of the enterprise.

Abstract: An embodiment for monitoring machine learning models to detect and rectify model drift using governance. The embodiment may receive a plurality of machine learning models and register the plurality of machine learning models to a governance dashboard. The embodiment may automatically monitor the received plurality of machine learning models to identify factors used by each of the received plurality of machine learning models and generate corresponding clusters of similar machine learning models. The embodiment may automatically detect an incorrect decision made by a target machine learning model and then automatically calculate a correlation score between the target machine learning model and machine learning models within an associated corresponding cluster of similar machine learning models. The embodiment may, in response to detecting a correlation score above a threshold, automatically determine and output a cluster reinforcement recommendation.

Abstract: An embodiment for managing data using machine learning models and information governance. The embodiment may automatically detect a data analysis request made within a system and identify subject datasets. The embodiment may automatically conduct shallow term assignments on each row and column of data in the subject datasets and automatically match the shallow term assignments for each row and column with a stored set of ranked terms, and automatically flag rows or columns matching with ranked terms above a predetermined threshold ranking for further analysis. The embodiment may automatically and continuously monitor and detect irrelevant metadata types to prevent subsequent analysis and storage of data including the irrelevant metadata types. The embodiment may automatically generate a criticality ranking for stored analysis datasets.