7. A non-transitory computer readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform operations comprising: receiving a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determining, by the processing device, a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; and in response to determining that the training data set has a satisfactory quality, analyzing, by the processing device, the training data set using machine learning to generate a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents.
7. A non-transitory computer readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform operations comprising: receiving a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determining, by the processing device, a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; and in response to determining that the training data set has a satisfactory quality, analyzing, by the processing device, the training data set using machine learning to generate a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents. 11. The non-transitory computer readable storage medium of claim 7 , wherein the received training data set is a single data set that does not distinguish between the plurality of sensitive documents or the plurality of non-sensitive documents, the operations further comprising: using local weighted latent semantic indexing (LSI) to divide the training data set into a plurality of distinct sets of documents; identifying a first distinct set of documents as containing the plurality of sensitive documents and a second distinct set of documents as containing the plurality of non-sensitive documents; and using machine learning with the first distinct set of documents and the second distinct set of documents to generate the machine learning-based detection (MLD) profile.