Symantec Corporation

Ca, Inc.

Sumesh Jaiswal

Phillip DiCorpo

Sally Kauffman

Alan Dale Galindez

Shitalkumar S Sawant

Ashish Aggarwal

Sarin Sumit Manmohan

Sumit Manmohan Sarin

Anindya Banerjee

Prahalad Gowardhan Deshpande

Rupesh Hanumant Khetawat

Amit Verma

Dhananjay Namdeo Dodke

Bishnu Chaturvedi

Rajorshi Ghosh Choudhury

Vipul Goel

Nikhil Sinha

Amit Dhotre

Earle Lowe

Arnaud Scomparin

Phillip Dicorpo

The disclosed computer-implemented method for encrypting files may include (i) detecting an event within a network that triggers an encryption of a file on the network, (ii) performing, in response to detecting the event, both encrypting the file to a file encryption key and encrypting the file encryption key to a public key of a source of the file, (iii) receiving, from a client, a file access request that includes the encrypted file encryption key, and (iv) transmitting, in response to determining that the client is authorized to access the file, a re-encrypted file encryption key to the client to enable the client to access the file. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for encrypting files

Techniques are disclosed for performing data loss prevention (DLP) by monitoring file system activity of an application having a network connection. A DLP agent tracks file system activity (e.g., file open and read operations) being initiated by the application. The DLP agent intercepts the file system activity and evaluates a file specified by the file system operation to determine whether the file includes sensitive data. If so determined, the DLP agent prevents the sensitive data from being transmitted (e.g., by blocking the file system activity, redacting the sensitive data from the file, etc.).

Preventing data loss over network channels by dynamically monitoring file system operations of a process

A computer-implemented method for data loss prevention of unidentifiable and unsupported object types may include (1) monitoring, through at least one filter, data input to an application during execution, (2) scanning, through a data loss prevention scanner, the data input to the application to detect whether the data includes sensitive data that is protected by a data loss prevention policy, (3) flagging, based on the scanning, the application as having accessed the sensitive data that is protected by the data loss prevention policy, (4) detecting that the application is requesting to output a data object in a format that obscures underlying content, and (5) performing, by a data loss prevention program, a remedial action to prevent loss of the sensitive data based on both flagging the application and detecting that the application is requesting to output the data object in the format that obscures underlying content.

Systems and methods for data loss prevention of unidentifiable and unsupported object types

Techniques for clipboard monitoring are disclosed. In one embodiment, the techniques may be realized as a method including identifying text that has been copied from an application to a clipboard; copying the identified text to a cache separate from the clipboard; replacing the identified text in the clipboard with a delayed clipboard object; in response to a reference call from the delayed clipboard object, analyzing the copied text for sensitive data; and in response to determining that the copied text includes sensitive data, providing a response to the reference call from the delayed clipboard object that does not include the copied text.

Techniques for clipboard monitoring

A computer-implemented method for applying data-loss-prevention policies. The method may include (1) maintaining a list of applications whose access to sensitive data is controlled by data-loss-prevention (DLP) policies, (2) detecting an attempt by a process to access sensitive data, (3) determining that the process has a parent-child relationship with an application within the list of applications, and (4) applying, based at least in part on the determination that the process has the parent-child relationship with the application, a DLP policy associated with the application to the process in order to prevent loss of sensitive data. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for applying data-loss-prevention policies

A computer-implemented method for providing targeted data loss prevention on unmanaged computing devices may include (1) identifying a data loss prevention policy that defines permissible data handling within set bounds to prevent unauthorized data exfiltration from the set bounds, (2) identifying an application to install on at least one unmanaged endpoint device, where (i) the unmanaged endpoint device lacks a data loss prevention agent configured to apply the data loss prevention policy to the entire unmanaged endpoint device and (ii) the application is to be provided to the unmanaged endpoint device to operate on sensitive data from within the set bounds, and (3) wrapping the application in an application wrapper that intercepts system calls from the application and applies the data loss prevention policy to sensitive data implicated in the system calls. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for providing targeted data loss prevention on unmanaged computing devices

A data loss prevention (DLP) manager running on a security virtual machine manages DLP policies for a plurality of guest virtual machines. The DLP manager identifies a startup event of a guest virtual machine, and installs a DLP component in the guest virtual machine. The DLP component communicates with the DLP manager operating within the security virtual machine. The DLP manager also receives file system events from the DLP component, and enforces a response rule associated with the guest virtual machine if the file system event violates a DLP policy.

System and method for data loss prevention in a virtualized environment

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 data loss prevention (DLP) manager running on a security virtual machine manages DLP policies for a plurality of guest virtual machines. The DLP manager identifies a source associated with a file open or create event. The source is at least one of an application or a device being used by a guest virtual machine (GVM). The DLP manager enforces a first response rule associated with the GVM when the source is a non-approved source per a source control policy. The DLP manager enforces a second response rule when the file violates a DLP policy.

Application and device control in a virtualized environment

A method and apparatus for identifying information as protected information using a structure is described. A DLP system, incorporating a structure analyzer, monitors outbound data transfers performed by the computing system for violations of a DLP policy. The DLP system analyzes a structure of information contained in an outbound data transfer against a protected structure defined in a DLP policy. The DLP system identifies the information as protected information to be protected by the DLP policy based on the analysis, and, when the information is identified as protected, the DLP system detects a violation of the DLP policy. The protected structure may be derived from document templates, document forms, or from a set of training documents.

Data loss prevention of information using structured document templates and forms

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 computer-implemented method may include (1) identifying a plurality of specific categories of sensitive information to be protected by a DLP system, (2) obtaining a training data set for each specific category of sensitive information that includes a plurality of positive and a plurality of negative examples of the specific category of sensitive information, (3) using machine learning to train, based on an analysis of the training data sets, at least one machine learning-based classifier that is capable of detecting items of data that contain one or more of the plurality of specific categories of sensitive information, and then (4) deploying the machine learning-based classifier within the DLP system to enable the DLP system to detect and protect items of data that contain one or more of the plurality of specific categories of sensitive information in accordance with at least one DLP policy of the DLP system.

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Pune, India

Sumesh Jaiswal