Onetrust, LLC

Vmware, Inc.

Airwatch LLC

Saravanan Pitchaimani

Kevin Jones

Jonathan Blake Brannon

Subramanian Viswanathan

Dylan D Patton-Kuhl

Ramana Malladi

Craig Farley Newell

Milap Shah

William DeWeese

Justin Devenish

Vijay Pitchumani Kodaganallur

Steven W Finch

Jeremy Turk

Haribalan Raghupathy

Mahesh Sivan

Arockia Gunasingam

Anand Balasubramanian

Matthew Hennig

Jonathan Lynn

Rahul Shinde

Zara Dana

Sivanandame Chandramohan

Abhishek Upadhyay

Aadil Allidina

Priya Malhotra

Mahashankar Sarangapani

Vishal Kalsi

Evan Bates

Ashok Kallarakuzhi

Vivek Srivastava

Various embodiments provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying targeted data for a data subject across a plurality of data objects in a data source. In accordance with one embodiment, a method is provided comprising: receiving a request to identify targeted data for a data subject; identifying a first data object using metadata for a data source that identifies the first data object as associated with a first targeted data type for a data portion from the request; identifying a first data field from a graph data structure of the first data object that identifies the first data field as used for storing data having the first targeted data type; and querying the first data object based on the first data field and the data for the first targeted data type to identify a first targeted data portion for the data subject.

Systems and methods for targeted data discovery

Aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for identifying data processing activities associated with various data assets based on data discovery results. In accordance various aspects, a method is provided comprising: identifying and scanning data assets to detect a subset of the data assets, wherein each asset of the subset is associated with a particular data element used for target data; generating a prediction for each pair of data assets of the subset on the target data flowing between the pair; identifying a data flow for the target data based on the prediction generated for each pair; and identifying a data processing activity associated with handling the target data based on a correlation identified for the particular data element, the subset, and/or the data flow with a known data element, subset, and/or data flow for the data processing activity.

Systems and methods for identifying data processing activities based on data discovery results

Aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for detecting prejudice bias in machine-learning models and/or data sets used in training, testing, and/or validating the models. In accordance various aspects, a method is provided comprising: receiving a data set used for training, testing, and/or validating a model that comprises data instances; generating, using a classification model, a prediction of applicability for each sub-category of a plurality of sub-categories for each bias category of a plurality of bias categories for each data instance; determining that a particular sub-category for a particular bias category is applicable to a proportion of the data set, wherein predictions of applicability for the particular sub-category generated for the proportion of the data set satisfies a threshold; and determining, based on the proportion, that the data set has a prejudice bias with respect to the particular bias category.

Systems and methods for detecting prejudice bias in machine-learning models

In general, various aspects provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing data discovery on a target computing system. In various aspects, a third party computing connects, via a public data network, to an edge node of the target computing system and instructs the target computing system to execute jobs to discover target data stored in data repositories in a private data network in the target computing system. In some aspects, the third party computing system may schedule the jobs on the target computing system based on computing resource availability on the target computing system.

Systems and methods for discovery, classification, and indexing of data in a native computing system

An application privacy analysis system is described, where the system obtains an application and analyzes it for privacy related data use. The system may determine privacy related activities of the application from established sources of such data and/or may decompile the application and analyze the resulting code to determine the privacy related activities of the application. The system may execute the application and monitor the communications traffic exchanged by the application to determine privacy related activities of the application. The system may store the results of such analyses for future reference.

Application privacy scanning systems and related methods

In particular embodiments, a sensitive data management system is configured to remove sensitive data after a period of non-use. Credentials used to access remote systems and/or third-party systems are stored with metadata that is updated with each use of the credentials. After a period of non-use, determined based on credential metadata, the credentials are deleted. Personal data retrieved to process a consumer request is stored with metadata that is updated with each use of the personal data. After a period of non-use, determined based on personal data metadata, the personal data is deleted. The personal data is also deleted if the system determines that the process or system that caused the personal data to be retrieved is no longer in use. An encrypted version of personal data may be stored for later use in verifying proper consumer request fulfillment.

Data processing systems and methods for automatically protecting sensitive data within privacy management systems

Examples described herein include systems and methods for controlling access to a server, such as an email server or a gateway, in situations where the identity of the requesting device is unknown or where the user device accesses the server using an unknown or unmanaged application. In one example, the system can utilize a user authentication credential included in the request to identify other devices belonging to the user that happen to be enrolled with the system. An out-of-band message can be sent to those enrolled devices, requesting confirmation from the user and, in conjunction with an authentication token, allowing the system to trust the previously unknown device. In the example of an unmanaged application attempting to access an email server, the system can confirm compliance of the requesting device and issue an authentication token that, along with an appropriate command sent to the email server, provides access.

Systems and methods for controlling email access

Systems herein allow a digital assistant to make requests to applications, such as third-party applications, that access data in an enterprise mobility management ('EMM') system. The digital assistant can link to a portal application and receive a token that identifies a user. A remote application on a user device can establish a session with the portal application as part of a single sign on ('SSO'). The session can identify the same user. The portal application can then link the digital assistant to the remote application. When the digital assistant makes a request to the portal application, a notification can be pushed to the remote application. The user can confirm the request, establishing an authorized session during which time the digital assistant can make additional requests to the portal application. The portal application can service the requests by accessing third-party applications available through the portal application and authorized for access by the SSO.

Securely managing digital assistants that access third-party applications

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for mapping the existence of target data within computing systems in a manner that does not expose the target data to potential data-related incidents. In accordance with various aspects, a method is provided that comprises: receiving a source dataset that comprises a label assigned to a data element used by a data source in handling target data that identifies a type of target data and data samples gathered for the data element; determining, based on the label, that the data samples are to be anonymized; generating supplemental anonymizing data samples associated with the label that comprise fictitious occurrences of the type of the target data; generating a review dataset comprising the supplemental anonymizing data samples intermingled with the data samples; and sending the review dataset to a review computing system.

Data processing systems and methods for anonymizing data samples in classification analysis

System and methods are disclosed for redacting analyzing unstructured data in a request for data associated with a data subject to determine whether the unstructured data is relevant to the request. The relevancy of pieces of the unstructured data may be determined by determining a categorization for each such piece of unstructured data and comparing them to known personal data associated with the data subject having the same categorization. Pieces of the unstructured data that do not match known personal data having the same categorization are redacted from the request before the request is processed.

Data processing systems and methods for automatically redacting unstructured data from a data subject access request

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Atlanta, GA, United States of America

Saravanan Pitchaimani