Abstract: Described herein is a third party data management system that uses a classification algorithm trained using a machine learning process to analyze type(s) of data that will be shared with the third party to determine a risk of sharing data with the third party. Periodically data provided to a particular third party can be analyzed to identify privacy issue(s). In response to the analysis, an action to be taken with respect to the particular third party can be identified and provided to a user. In some embodiments, information from trusted news feeds can be processed using natural language processing to determine a potential privacy or security issue regarding a third party with whom data has been shared.

Abstract: Techniques for tracking data flows in an organization are provided. According to one set of embodiments, a computer system can receive a message indicating injection of an artificial data record (i.e., dye record) into a first data store of an organization, where the message includes a unique identifier associated with the artificial data record and an identifier of the first data store. The computer system can further scan a plurality of data stores of the organization for the unique identifier and, upon finding the unique identifier in a second data store of the organization that is different from the first data store, generate data flow information for the organization indicating a data flow from the first data store to the second data store and verify one or more policies of the organization based on the data flow information.