Onetrust, LLC

Ken A Browne

Jonathan Blake Brannon

Kabir A Barday

Jason L Sabourin

Mihir S Karanjkar

Dylan D Patton-Kuhl

Steven W Finch

Aakash H Patel

Richard L Daniel

Nathan W Heard

Kevin Jones

Priya Malhotra

In various embodiments, a data map generation system is configured to receive a request to generate a privacy-related data map for particular computer code, and, at least partially in response to the request, determine a location of the particular computer code, automatically obtain the particular computer code based on the determined location, and analyze the particular computer code to determine privacy-related attributes of the particular computer code, where the privacy-related attributes indicate types of personal information that the particular computer code collects or accesses. The system may be further configured to generate and display a data map of the privacy-related attributes to a user.

Data processing systems and methods for performing assessments and monitoring of new versions of computer code for compliance

Data processing systems and methods according to various embodiments are adapted for automatically detecting and documenting privacy-related aspects of computer software. Particular embodiments are adapted for: (1) automatically scanning source code to determine whether the source code include instructions for collecting personal data; and (2) facilitating the documentation of the portions of the code that collect the personal data. For example, the system may automatically prompt a user for comments regarding the code. The comments may be used, for example, to populate: (A) a privacy impact assessment; (B) system documentation; and/or (C) a privacy-related data map. The system may comprise, for example, a privacy comment plugin for use in conjunction with a code repository.

Data processing systems and methods for automatically detecting and documenting privacy-related aspects of computer software

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 data processing data inventory generation system is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include, for example, any entity that collects, processes, contains, and/or transfers personal data (e.g., such as a software application, 'internet of things' computerized device, database, website, data-center, server, etc.). For example, a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.

Data processing systems for generating and populating a data inventory

In particular embodiments, a data processing data inventory generation system is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include any entity that collects, processes, contains, and/or transfers personal data (e.g., a software application, database, website, server, etc.). A data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc. The system may then utilize the generated model to fulfil a data subject access request.

Data processing systems for generating and populating a data inventory for processing data access requests

In particular embodiments, a data processing data inventory generation system is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include, for example, any entity that collects, processes, contains, and/or transfers personal data (e.g., such as a software application, 'internet of things' computerized device, database, website, data-center, server, etc.). The system may be configured to identify particular data assets and/or personal data in data repositories using any suitable intelligent identity scanning technique.

Data processing and scanning systems for generating and populating a data inventory

A computer-implemented method for populating a privacy-related data model by: (1) providing a data model that comprises one or more respective populated or unpopulated fields; (2) determining that at least a particular one of the fields for a particular data asset is an unpopulated field; (3) at least partially in response to determining that the at least one particular field is unpopulated, automatically generating a privacy questionnaire comprising at least one question that, if properly answered, would result in a response that may be used to populate the at least one particular unpopulated field; (4) transmitting the privacy questionnaire to at least one individual; (5) receiving a response to the questionnaire, the response comprising a respective answer to the at least one question; and (6) in response to receiving the response, populating the at least one particular unpopulated field with information from the received response.

Data processing systems and methods for performing privacy assessments and monitoring of new versions of computer code for privacy compliance

In various embodiments, a Data Model Adaptive Execution System may be configured to take one or more suitable actions to remediate an identified risk in view of one or more regulations (e.g., one or more legal regulations, one or more binding corporate rules, etc.). For example, in order to ensure compliance with one or more standards related to the collection and/or storage of personal data, an entity may be required to modify one or more aspects of a way in which the entity collects, stores, and/or otherwise processes personal data (e.g., in response to a change in a legal or other requirement). In order to identify whether a particular change or other risk trigger requires remediation, the system may be configured to assess a relevance of the risk posed by the risk and identify one or more processing activities or data assets that may be affected by the risk.

Computer systems and methods for: (1) analyzing electronic correspondence associated with a data subject (e.g., the emails within one or more email in-boxes associated with the data subject); (2) based on the analysis, identifying at least one entity that that the data subject does not actively do business with (e.g., as evidenced by the fact that the data subject no longer opens emails from the entity, and/or has set up a rule to automatically delete emails received from the entity); and (3) in response to identifying the entity as an entity that the data subject no longer does business with, at least substantially automatically populating and/or submitting a data subject access request to the entity (e.g., to delete all personal information being processed by the entity).

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Johns Creek, GA, United States of America

Ken A Browne