Abstract: Disclosed are various embodiments for augmented intelligent machine for systematic attribution of data security. A set of global regulations is received by the system. Next, at least one decision rule for mapping one or more data elements is output based at least in part on the set of dynamic global regulations. A data input is received from at least one data source. Next, the data input is parsed to determine at least one sensitive data elements. Then, a confidentiality level of at least one sensitive data element is determined. The ingesting table is joined with a drive mapping data set. Finally, at least one security policy is applied to a type of data input based at least in part on the drive mapping data set or the confidentiality level.

Abstract: Disclosed are various embodiments for augmented intelligent machine for systematic attribution of data security. A set of global regulations is received by the system. Next, at least one decision rule for mapping one or more data elements is output based at least in part on the set of dynamic global regulations. A data input is received from at least one data source. Next, the data input is parsed to determine at least one sensitive data elements. Then, a confidentiality level of at least one sensitive data element is determined. The ingesting table is joined with a drive mapping data set. Finally, at least one security policy is applied to a type of data input based at least in part on the drive mapping data set or the confidentiality level.

Abstract: A method, system, and computer readable storage to implement a data marketplace which stored data assets, such as tables, models, variables, etc. All of the data assets (assets) can be searched and retrieved from the data marketplace notwithstanding that the assets can be stored at different locations, in different forms, and in different platforms through an entire big data system. The data marketplace also predicts and suggests assets which will be likely to be relevant to a user's current project.