Symantec Corporation

Ca, Inc.

Alan Dale Galindez

Sally Kauffman

Sumesh Jaiswal

Phillip DiCorpo

Shitalkumar S Sawant

Ashish Aggarwal

Harold Byun

Adam Jackson

Jeremy Mailen

Pritesh Sharma

Philip Sarin

Himanshu Bari

Phillip Dicorpo

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.

User interface and workflow for performing machine learning

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.

Data quality assessment for vector machine learning

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.

Incremental machine learning for data loss prevention

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.

Sensitive file accessing trends can be visually represented. In one embodiment, the present invention includes receiving, in a graphical user interface (GUI), a first user input indicating a selection of one folder from a plurality of folders, and identifying a plurality of sensitive files in the selected folder using one or more data loss prevention policies. In one embodiment access usage data for the sensitive files can be obtained and the number of users accessing the sensitive files within a plurality of time intervals can be determined using the access usage data. Finally, a usage trend representation comprising a visual illustration of the number of users accessing the sensitive files within each of the plurality of time intervals can be displayed to a user.

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Menlo Park, CA, United States of America

Alan Dale Galindez