| Abstract | With the rapid growth of cloud computing and remote workforces, organizations are increasingly aware of the risk of data leaks and data exfiltration. Handling such risk has become more challenging because organizations today usually deal with big data. In particular, organizations must deal with massive amounts of unstructured data. As a result, modern data leak prevention (DLP) solutions must support automated methods to detect and identify confidential and sensitive information in both structured and unstructured data. In this paper, we demonstrate the benefits of using deep learning to identify unstructured context-dependent sensitive information, in contrast to traditional machine learning methods and rule-based methods. |
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