Abstract: Machine-learning based detection (MLD) profiles can be used to identify sensitive information in documents. The MLD profile can be used to generate a confidence value for the document that expresses the degree of confidence with which the MLD profile can classify the document as sensitive or not. In one embodiment, a data loss prevention system provides or suggests a confidence level threshold to a user of the data loss prevention system by providing a confidence level threshold for the MLD profile to the user, the confidence level threshold to be used as the boundary between sensitive data and non-sensitive data. In one embodiment the provided confidence level threshold is determined by scanning a random data set using the MLD profile.

Abstract: A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device computes a quality metric for the MLD profile.

Abstract: A computing device receives a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents. The computing device determines a quality of the training data set. The quality may be determined using k-fold cross validation and/or latent semantic indexing. In response to determining that the training data set has a satisfactory quality, the computing device then analyzes the training data set using machine learning to train 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.

Abstract: A computing device receives a document that was incorrectly classified as sensitive data based on a machine learning-based detection (MLD) profile. The computing device modifies a training data set that was used to generate the MLD profile by adding the document to the training data set as a negative example of sensitive data to generate a modified training data set. The computing device then analyzes the modified training data set using machine learning to generate an updated MLD profile.

Abstract: A computing device receives a document that was incorrectly classified as sensitive data based on a machine learning-based detection (MLD) profile. The computing device modifies a training data set that was used to generate the MLD profile by adding the document to the training data set as a negative example of sensitive data to generate a modified training data set. The computing device then analyzes the modified training data set using machine learning to generate an updated MLD profile.

Abstract: A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device computes a quality metric for the MLD profile.

Abstract: A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device displays a quality metric for the MLD profile in the user interface.

Abstract: A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device displays a quality metric for the MLD profile in the user interface.