MACHINE LEARNING MODELING FOR PROTECTION AGAINST ONLINE DISCLOSURE OF SENSITIVE DATA

Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score. RECEIVE A SET OF UNSTRUCTURED TEXT DATA 204\ RECEIVE AN IMAGE OR VIDEO FILE IN ASSOCIATION WITH THE UNSTRUCTURED TEXT DATA PROCESS THE IMAGE OR VIDEO FILE TO IDENTIFY METADATA 208\I PROCESS THE TEXT DATA AND THE METADATA TO IDENTIFY A PLURALITY OF ENTITIES ASSOCIATED WITH PRIVATE INFORMATION USING A TRAINED MACHINE LEARNING MODEL COMPUTE A PRIVACY SCORE FOR THE TEXT DATA BY IDENTIFYING CONNECTIONS BETWEEN THE ENTITIES UPDATE THE GRAPHICAL INTERFACE TO INCLUDE AN INDICATOR DISTINGUISHING A TARGET PORTION OF THE SET OF UNSTRUCTURED TEXT DATA WITHIN THE INPUT FIELD FROM OTHER PORTIONS OF THE SET OF UNSTRUCTURED TEXT DATA WITHIN THE INPUT FIELD A MODIFICATION TO THE TARGET PORTION CHANGES THE POTENTIAL EXPOSURE OF THE PRIVATE INFORMATION INDICATED BY THE PRIVACY