Onetrust, LLC

Airwatch LLC

Vmware, Inc.

Kevin Jones

Jonathan Blake Brannon

Kabir A Barday

Jason L Sabourin

Mihir S Karanjkar

Dylan D Patton-Kuhl

Steven W Finch

Bryan Patrick Kveen

Richard A Beaumont

Nicholas Ian Pavlichek

Saravanan Pitchaimani

Eliza Rose Crawford

Aakash H Patel

Ken A Browne

Richard L Daniel

Nathan W Heard

Subramanian Viswanathan

Casey Hill

Milap Shah

Ramana Malladi

Patrick Whitney

Jeremy Turk

Anand Balasubramanian

Priya Malhotra

Mithun Babu

Chase Bradley

William DeWeese

Justin Devenish

Mahashankar Sarangapani

Adam Stephen Rykowski

Abhishek Mishra

Nishita Manjunath

Jeffrey Baucom

Madhusudhan Kunhambu

Rajneesh Kesavan

Sai Kiran Vudutala

Jonathan Spaeth

Arockia Gunasingam

Haribalan Raghupathy

Mahesh Sivan

Brian Kelly

Sathish Gopalakrishnan

Mohamed Kabad

Jayamohan Puthenveetil

Prashanth Sharma

Himanshu Arora

Atul Gupta

Sharath Chandra Chavva

Prabhu Kumar

Pratik Doshi

Matthew Hennig

Jonathan Lynn

Rahul Shinde

Zara Dana

Sivanandame Chandramohan

Abhishek Upadhyay

Aadil Allidina

Santosh Kumar Koti

Naveen Pitchandi

Santhosh Kumar Koti

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

In particular embodiments, a consent notice configuration determination system may be configured to: (1) scan a particular website from a plurality of different locations; (2) identify one or more types of technologies available on the particular website for individuals accessing the particular website from each of the plurality of different locations; (3) determine, for each of the plurality of different locations, based on a global set of databases, legal/regulatory guidance, etc. and the one or more types of technologies, particular legal and industry requirements for each of the plurality of different locations; and (4) automatically configure, for each of the plurality of different locations, a consent interface for the particular website for each of the plurality of different locations based at least in part on the one or more types of technologies and the global set of databases.

Data processing systems and methods for dynamically determining data processing consent configurations

Data processing systems and methods, according to various embodiments, are adapted for determining a categorization for each tracking tool that executes on a particular webpage based on a variety of criteria, such as the purpose of the tracking tool and its source script. The system may compare the characteristics of tracking tools on a webpage to a database of known tracking tools to determine the appropriate categorization. When a user visits the webpage, the system analyzes these categories and determines whether the tracking tool should be permitted to run based on the categories and/or other criteria, such as whether the user has consented to the use of that type of tracking tool.

Data processing systems and methods for automatically blocking the use of tracking tools

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

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

Embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for verifying the identity of a data subject. In one embodiment, a method is provided comprising: receiving, via a browser, a consumer rights request for a data subject for performing an action with regard to personal data associated with the data subject; detecting a state of the browser indicating a location; identifying a law based on the location; determining a level of identity verification required based on the law; generating, based on the level, a GUI by configuring a first prompt on the GUI configured for receiving input for a first type of identity verification; transmitting an instruction to present the GUI; receiving the input for the first type of identity verification; verifying the identity of the data subject based on the input; and responsive to verifying the identity, causing performance of the action.

Data processing systems for identity validation for consumer rights requests and related methods

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 various embodiments, a system may be configured to substantially automatically determine whether to take one or more actions in response to one or more identified risk triggers (e.g., data breaches, regulation change, etc.). The system may, for example: (1) compare the potential risk trigger to one or more previous risks triggers experienced by the particular entity at a previous time; (2) identify a similar previous risk trigger (e.g., one or more previous risk triggers related to a similar change in regulation, breach of data, type of issue identified, etc.); (3) determine the relevance of the current risk trigger based at least in part on a determined relevance of the previous risk trigger; and (4) determine whether to take one or more actions to the current risk trigger based at least in part on one or more determined actions to take in response to the previous, similar risk trigger.

Data processing systems for identifying, assessing, and remediating data processing risks using data modeling techniques

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

Responding to a data subject access request includes receiving the request and identifying the requestor and source. In response to identifying the requestor and source, a computer processor determines whether the data subject access request is subject to fulfillment constraints, including whether the requestor or source is malicious. If so, then the computer processor denies the request or requests a processing fee prior to fulfillment. If not, then the computer processor fulfills the request.

Data processing systems for fulfilling data subject access requests and related methods

In particular embodiments, a Personal Data Deletion System is configured to: (1) at least partially automatically identify and delete personal data that an entity is required to erase under one or more of the conditions discussed above; and (2) perform one or more data tests after the deletion to confirm that the system has, in fact, deleted any personal data associated with the data subject. The system may, for example, be configured to test to ensure the data has been deleted by: (1) submitting a unique token of data through a form to a system; (2) in response to passage of an expected data retention time, test the system by calling into the system after the passage of the data retention time to search for the unique token.

Data processing systems for data testing to confirm data deletion and related methods

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Atlanta, GA, United States of America

Kevin Jones