Systems and methods for classifying documents for data loss prevention

摘要

A computer-implemented method for classifying documents for data loss prevention may include 1) identifying training documents for a machine learning classifier configured for data loss prevention, 2) performing a semantic analysis on training documents to identify topics within the set training documents, 3) applying a similarity metric to the topics to identify at least one unrelated topic with a similarity to the other topics within the plurality of topics, as determined by the similarity metric, that falls below a similarity threshold, 4) identifying, based on the semantic analysis, at least one irrelevant training document within the set of training documents in which a predominance of the unrelated topic is above a predominance threshold, and 5) excluding the irrelevant training document from the set of training documents based on the predominance of the unrelated topic within the irrelevant training document. Various other methods, systems, and computer-readable media are also disclosed.

主权项

1. A computer-implemented method for classifying documents for data loss prevention, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising:
identifying a set of prospective training documents for a machine learning classifier that is configured to provide input for data loss prevention determinations, the set of prospective training documents comprising documents regarded as sensitive; performing a semantic analysis on the set of prospective training documents to identify a plurality of topics within the set of prospective training documents; applying a similarity metric to the plurality of topics to identify at least one unrelated topic within the plurality of topics with a similarity to the other topics within the plurality of topics, as determined by the similarity metric, that falls below a predetermined similarity threshold, and to thereby determine that the unrelated topic is unrelated to the other topics within the plurality of topics; identifying, based at least in part on the semantic analysis, at least one irrelevant prospective training document within the set of prospective training documents in which a predominance of the unrelated topic is above a predetermined predominance threshold by determining the predominance of the unrelated topic by the presence of the unrelated topic within the irrelevant prospective training document as identified in the semantic analysis and not by the presence within the irrelevant prospective training document of any related topic within the plurality of topics; excluding the irrelevant prospective training document from the set of prospective training documents based at least in part on the predominance of the unrelated topic within the irrelevant prospective training document.