Method For Identifying Confidential Data Using Unsupervised Machine Learning In Data Leakage Prevention

Abstract

In today’s business world, many organizations use information systems to manage their confidential information. The need to protect confidential information of the organization is very critical. Data leakage threat has become an important issue especially data leakage caused by insiders in the organizations. Data Leakage Prevention (DLP) is one of the methods for effectively preventing data leakages. Data leakage prevention system (DLP) is a system, stops transfer of confidential data from organization’s network to outside world. DLP solutions must be able to identify and protect confidential data within organization. Content-aware DLP is one of the DLP solution can read all the data contained within the file, identify confidential data and provide protection to the organizations data. Content-aware DLP solutions with context information properly classify confidential data and provide more protection to the organization data. The proposed invention prevents data leakages caused by insiders of the organization using context of the content. The existing data leakage prevention methods, Keyword based, Phrase based and Statistical methods identifies the confidentiality of the document based on specific keywords, phrases or statistical values. The keyword, phrase based methods ignore the context of the keyword while statistical methods ignore the content of the analyzed text. 5 claims & 1 Figure

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