BACKGROUND

The present invention relates generally to the field of telecommunication and more particularly to securing attachments associated with electronic communication.

Electronic messaging has become ubiquitous as a form of communication between individuals in the social and/or business capacity. In a business capacity, email has been used superfluously throughout business organizations. Furthermore, files (e.g., spreadsheets, pictures, etc.) have been attached to emails correspondence within all organization. Attachments could include salary information, forecast sales figure, presentation outlining a company's business strategy.

Reputational risk is the potential loss to financial capital, social capital and/or market share resulting from damages to a firm's reputation. The cost of accidental data breach (e.g., electronic/soft copy, hard copy, etc.) for companies reaches thousands of dollars a year per incident, and even more in reputation risk and damage. Thus, when an email with sensitive attachment (i.e., forecast sales figure) is sent to the wrong recipient, the risk of accidentally sharing confidential competitor and internal data is heightened.

SUMMARY

Aspects of the present invention disclose a computer-implemented method, computer program product, and computer system for processing documents to ameliorate inadvertent sharing of sensitive information during electronic communications. The computer implemented method includes determining a first context, by leveraging machine learning, of an electronic communication being prepared for transmission; determining a historical context, by leveraging machine learning, of the electronic communication; determining, by leveraging machine learning, one or more risk scores based on the first context and the historical context; assigning, by leveraging machine learning, the one or more risk scores to the first context and the historical context; determining, by leveraging machine learning, whether the one or more risk scores exceed one or more predetermined thresholds based on machine learning; and responsive to the one or more risk scores exceeding the one or more predetermined thresholds, executing, by leveraging machine learning, an action plan.

In another embodiment, the computer program product includes one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: program instructions to determine a first context, by leveraging machine learning, of an electronic communication being prepared for transmission; program instructions to determine a historical context, by leveraging machine learning, of the electronic communication; program instructions to determine, by leveraging machine learning, one or more risk scores based on the first context and the historical context; program instructions to assign, by leveraging machine learning, the one or more risk scores to the first context and the historical context; program instructions to determine, by leveraging machine learning, whether the one or more risk scores exceed one or more predetermined thresholds based on machine learning; and responsive to the one or more risk scores exceeding the one or more predetermined thresholds, program instructions to execute, by leveraging machine learning, an action plan.

In another embodiment, the computer system includes one or more computer processors; one or more computer readable storage media; program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to determine a first context, by leveraging machine learning, of an electronic communication being prepared for transmission; program instructions to determine a historical context, by leveraging machine learning, of the electronic communication; program instructions to determine, by leveraging machine learning, one or more risk scores based on the first context and the historical context; program instructions to assign, by leveraging machine learning, the one or more risk scores to the first context and the historical context; program instructions to determine, by leveraging machine learning, whether the one or more risk scores exceed one or more predetermined thresholds based on machine learning; and responsive to the one or more risk scores exceeding the one or more predetermined thresholds, program instructions to execute, by leveraging machine learning, an action plan.

