Methods, systems, and non-transitory computer readable storage media are disclosed for updating the priority of classifiers in a classifier model. Specifically, the disclosed systems execute operations to extract data elements from a digital dataset. The disclosed system generates first classifier labels for a first subset of data elements (e.g., a test dataset) by utilizing a classification model to apply a predetermined order of classifiers to the first subset of data elements. The disclosed systems utilize the first classifier labels to determine a priority order for the classifiers for applying to a second subset of data elements the digital dataset. Using the determined priority order of the classifiers, the disclosed systems can generate second classifier labels for a second subset of data elements by utilizing the classifier model to apply the classifiers according to the priority order.
Methods, systems, and non-transitory computer readable storage media are disclosed for routing digital content items to priority-based processing queues based on classifications of the digital content items according to one or more system requirements frameworks. Specifically, the disclosed system scans and classifies digital content items at a digital data repository based on data types included in the digital content items. The disclosed system utilizes a classification model with a classification profile to classify the digital content items according to one or more system requirements frameworks and routes the digital content items to priority-based processing queues according to priority levels indicated by the classifications. Furthermore, the disclosed system provides indications of classifications of the portions of the digital content items (e.g., to indicate high priority data). The disclosed system can also perform additional computing operations on the digital content items according to the routing via the priority-based processing queues.
Methods, systems, and non-transitory computer readable storage media are disclosed for managing implementation of machine-learning models within computing environments according to system requirements frameworks via common data objects. The disclosed system generates a common data object to represent an implementation of a machine-learning model with a data process. For example, the disclosed system determines attribute values of the common data object according to data objects representing the machine-learning model and related datasets. Furthermore, the disclosed system utilizes the common data object to validate the machine-learning model according to a digital representation of a system requirements framework that includes usage requirements for machine-learning models to store, process, transmit, or otherwise handle specific data types in specific ways for the one or more data processes within a computing environment. The disclosed systems also perform operations to implement, suspend, or otherwise modify the machine-learning model or datasets based on the validation.
G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
Aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for implementing and managing access to particular data based on access controls for implementing purpose restrictions and/or consent restrictions. In various aspects, a method is provided that comprises: receiving a request transmitted by an application executing on a client computing system and requesting access to a dataset, wherein each data record of the dataset comprises data elements; identifying, based on the application, a purpose for the application requesting access to the dataset; referencing, based on the purpose, an applicable purpose-based access-control policy to identify an authorization token; and providing the authorization token, wherein the storage computing system provides the client computing system with a view of the dataset based on the token with the view having a data element returning modified data in a manner compliant with the applicable purpose-based access-control policy.
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating multidimensional risk visualizations depicting severity and frequency and for predicting risk mitigation strategies. For example, the disclosed systems generate multidimensional risk visualizations that present visual representations of risk severity and risk frequency in multidimensional formats, including many risk dimensions at once. In certain cases, the disclosed systems further utilize a particular machine learning model such as a strategy prediction neural network to generate predicted mitigation strategies based on risk data.
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing machine-learning models to deduplicate electronic survey questions of electronic surveys or questionnaires in real-time. Specifically, the disclosed system maps electronic survey questions to specific domain classifications by utilizing a machine-learning model to classify portions of electronic surveys based on context within the portions of the electronic surveys. Additionally, the disclosed system utilizes the mappings of electronic survey questions to domain classifications to determine whether to deduplicate specific questions that are semantically similar and within the same domain classifications. For instance, the disclosed system utilizes natural language processing to find semantically similar questions across a plurality of electronic surveys and deduplicate the similar questions if their domain classifications are the same.
Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations modifying physical emissions sources of an entity based on past and modeled emissions for the entity. Specifically, the disclosed system monitors emissions produced by an entity by determining a number of emissions sources corresponding to an entity and a plurality of emissions values for the emissions sources. Additionally, the disclosed system determines a plurality of constraints corresponding to the entity. The disclosed system also determines goals for the entity including target emissions values. The disclosed system utilizes a modified gradient descent model to iteratively adjust emissions values for the physical emissions sources to obtain the target emissions values according to the constraints. The disclosed system generates action recommendations for modifying the physical emissions sources utilizing the modified gradient descent model and provides the action recommendations for display within a graphical user interface.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
8.
GENERATING ACTION RECOMMENDATIONS FOR MODIFYING PHYSICAL EMISSION SOURCES BASED ON MANY SIMULATIONS OF DIFFERENT SCENARIOS UTILIZING A MODIFIED GRADIENT DESCENT MODEL
Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for modifying physical emissions sources based on a plurality of simulations of different scenarios utilizing a modified gradient descent model. Specifically, the disclosed system utilizes the modified gradient descent model to generate emissions value modifications for physical emissions sources corresponding to an entity based on a set of constraints and target emissions values. The disclosed system runs a plurality of simulations to generate modified target emissions values, utilizing the modified gradient descent model, by modifying source attributes of the physical emissions sources according to a plurality of probability distributions representing source attributes of the physical emissions sources. The disclosed system then compares the initial target emissions values to the modified target emissions values determined from the simulations to generate action recommendations for modifying the physical emissions sources.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
9.
GENERATING FORECASTED EMISSIONS VALUE MODIFICATIONS AND MONITORING FOR PHYSICAL EMISSIONS SOURCES UTILIZING MACHINE-LEARNING MODELS
Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications. In additional embodiments, the disclosed system tracks emissions of the entity during the future time period and generate additional action recommendations in response to detecting deviations from forecasted emissions production.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
10.
AUTO-BLOCKING SOFTWARE DEVELOPMENT KITS BASED ON USER CONSENT
Aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for auto-blocking of software development kit functionality for mobile software applications based on consent (or lack thereof) provided by users who are interacting with the mobile software applications.
The present disclosure provides methods, apparatus, systems, computing devices, computing entities, and/or the like for providing persistent representations in graph data structures of relationships that exist among data objects found across different data-related processes to enable efficient querying of data from the different data-related processes.
In various aspects, a data transfer discovery and analysis system may query an entity computing system to identify access credentials for third-party computing systems and scan each access credential to determine associated permissions provided by each access credential on the entity computing system. The data transfer discovery and analysis system may further inspect access logs to identify actual data transfers between the entity computing system and third-party computing systems as well as other access activity associated with each of the credentials. The system can generate and store a mapping of all actual data transfers (e.g., based on the access log data) and potential data transfers (e.g., based on particular access permissions) between/among the entity computing system and the third-party computing systems. By analyzing access logs to determine actual data transfers executed under each particular access credential, the data transfer discovery and analysis system can identify unused and/or underutilized access permissions.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
A privacy-related consent extension and data processing system may be configured to automatically extend one or more privacy-related consents for a user of a first computing device to a second computing device. In various embodiments, the system is configured to provide a computer-readable indicium(indicia) on a previously unknown computing device upon initiation of a transaction between a user and an entity collecting and processing privacy data. In response to a user using a known computing device to scan the computer-readable indicium, in various embodiments, the system may provide the ability to share user consent data provided by the first known device to the second unknown device, allowing the user to provide consent without manually re-entering privacy and consent preferences.
In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for generating and managing custom workflows for domain objects defined within microservices. In accordance with various aspects, a method is that comprises: receiving an attribute value for a custom workflow to include in a microservice that corresponds to an attribute defined for a workflow component; accessing mapping data for an attribute; identifying, based on the mapping data, a corresponding field of a workflows table mapped to the attribute; storing a record in the workflows table for the custom workflow with the attribute value stored in the corresponding field for the record to persist the custom workflow in the microservice.
In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for identifying and documenting certain subject matter found in media content, as well as selectively redacting the subject matter found in media content. In accordance with various aspects, a method is provided that comprises: obtaining first metadata for media content, the metadata identifying a context and a portion of content for an individual; identifying, based on the context, a certain subject matter for the media content; determining that a particular item associated with the subject matter is present in the portion of content associated with the individual; generating second metadata to document the particular item present in the portion of content; and using the second metadata to selectively redacting the item from the portion of content associated with the individual upon request to do so with respect to the individual.
In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for integrating third party computing system functionality into a first party computing system by providing a risk management and mitigation computing system configured to analyze a risk of integrating the functionality provided by the third party computing system and facilitating implementation of one or more data-related controls that include performing computer-specific operations to mitigate and/or eliminate the identified risks. For example, the risk management and mitigation computing system can access risk data in tenant computing systems to determine a risk score related to the integration of the third party computing system functionality based on risks determined during prior integrations of the third party computing system functionality by other tenant computing systems. The risk management and mitigation computing system can generate a recommended control when integrating the third party computing system functionality.
In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for generating and managing custom attributes and corresponding values for domain objects defined within microservices. In accordance with various aspects, a method is provided that comprises: receiving a custom attribute request for a domain object defined in a microservice that comprises a domain object identifier, a custom attribute to add to the domain object, and a value type for the custom attribute; identifying, based on the domain object identifier and the value type, a custom value table for the domain object that comprises placeholder fields; identifying a placeholder field that is available in the custom value table; and storing a record in the attribute schema table for the domain object and the custom attribute comprising mapping data that maps the custom attribute to the placeholder field for the custom value table.
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.
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.
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.
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 are disclosed detecting whether calls to consent rejection functions originate with an automated tool or a human user. The system can determine that a calls to a consent rejection function are likely from an automated tool by determining that a rate and/or number of calls to a function exceeds a threshold and/or that the calls are received before the interface requesting user consent preferences has been rendered to the user. The system can also require that a function call include a token that an automated tool would not have knowledge of or access to and reject function calls without this token. The system can also use private consent rejection function calls with obfuscated names and/or provide a follow up consent rejections confirmation interface requiring human user input before process a consent rejection.
Aspects of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for protection of system software, or data from destruction, unauthorized modification, and/or unauthorized disclosure securing by, for example, detecting the transfer and/or processing of target data. Accordingly, a method is provided that involves: scanning a software application to identify functionality configured for processing target data; identifying fields associated with the functionality; identifying metadata associated with a field; generating, from the metadata, an identification of a type of data associated with the field; determining a location based on the processing of the target data by the functionality; determining a risk associated with the functionality processing the target data based on the location and the type of data; determining that the risk satisfies a threshold level of risk; and in response, causing an action to be performed to mitigate the risk.
G06F 21/55 - Detecting local intrusion or implementing counter-measures
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
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.
Embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for permitting or blocking tracking tools used through webpages. In particular embodiments, the method involves: scanning a webpage to identify a tracking tool configured for processing personal data; determining a data destination location that is associated with the tracking tool; and generating program code configured to: determine a location associated with a user who is associated with a rendering of the webpage; determine a prohibited data destination location based on the location associated with the user; determine that the data destination location associated with the tracking tool is not the prohibited data destination location; and responsive to the data destination location associated with the tracking tool not being the prohibited data destination location, permit the tracking tool to execute.
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.
A mobile application privacy analysis system is described, where the system scans a mobile device to identify files associated with a particular SDK and generates a tokenized name for the SDK. The tokenized name includes tokens representing the SDK vendor and one or more functions of the SDK. Using the tokenized name, the system then determines corresponding categories for each functionality token and score for each such category. Based on the scores, the system determines the most significant category and assigns that category to the SDK for use in privacy analysis. The system may also, or instead, determine a vendor category using the vendor token and assign that category to the SDK. Weighting factors may be applied to the scores for the categories associated with the functionality tokens and vendor tokens.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
28.
DATA PROCESSING SYSTEMS FOR USE IN AUTOMATICALLY GENERATING, POPULATING, AND SUBMITTING DATA SUBJECT ACCESS REQUESTS
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 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).
In particular embodiments, a Data Access Webform Crawling System is configured to: (1) identify a webform used to collect one or more pieces of personal data; (2) robotically complete the identified webform; (3) analyze the completed webform to determine one or more processing activities that utilize the one or more pieces of personal data collected by the webform; (4) identify a first data asset in the data model that is associated with the one or more processing activities; (5) modify a data inventory for the first data asset in the data model to include data associated with the webform; and (6) modify the data model to include the modified data inventory for the first data asset.
In particular embodiments, a Cross-Border Visualization Generation System is configured to: (1) identify one or more data assets associated with a particular entity; (2) analyze the one or more data assets to identify one or more data elements stored in the identified one or more data assets; (3) define a plurality of physical locations and identify, for each of the identified one or more data assets, a respective particular physical location of the plurality of physical locations; (4) analyze the identified one or more data elements to determine one or more data transfers between the one or more data systems in different particular physical locations; (5) determine one or more regulations that relate to the one or more data transfers; and (6) generate a visual representation of the one or more data transfers based at least in part on the one or more regulations.
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.
A consent receipt management system may, for example, be configured to track data on behalf of an entity that collects and/or processes persona data related to: (1) who consented to the processing or collection of personal data; (2) when the consent was given (e.g., a date and time); (3) what information was provided to the consenter at the time of consent (e.g., a privacy policy, what personal data would be collected following the provision of the consent, for what purpose that personal data would be collected, etc.); (4) how consent was received (e.g., one or more copies of a data capture form, webform, etc. via which consent was provided by the consenter); (5) when consent was withdrawn (e.g., a date and time of consent withdrawal if the consenter withdraws consent); and/or (6) any other suitable data related to receipt or withdrawal of consent.
A centralized data repository system, in various embodiments, is configured to provide a central data-storage repository (e.g., one or more servers, databases, etc.) for the centralized storage of personally identifiable information (PII) and/or personal data for one or more particular data subjects. In particular embodiments, the centralized data repository may enable the system to populate one or more data models (e.g., using one or more suitable techniques described above) substantially on-the-fly (e.g., as the system collects, processes, stores, etc. personal data regarding a particular data subject). In this way, in particular embodiments, the system is configured to maintain a substantially up-to-date data model for a plurality of data subjects (e.g., each particular data subject for whom the system collects, processes, stores, etc. personal data).
Various Data Subject Access Request (DSAR) processing systems are adapted for presenting a first webform on a first website, the first webform being adapted to receive DSAR's and to route the requests to a first designated individual for processing; presenting a second webform on a second website, the second webform being adapted to receive DSAR's and to route the requests to a second designated individual for processing; receiving, via the first webform, a first DSAR; at least partially in response to the receiving the first DSAR, automatically routing the first DSAR to the first designated individual for handling; receiving, via the second webform, a second DSAR; at least partially in response to the receiving the second DSAR, automatically routing the second DSAR to the second designated individual for handling; and communicating a status of both the first DSAR and the second DSAR via a single user interface.
Responding to a data subject access request includes receiving the request and validating an identity of the requestor. In response to validating the identity of the requestor, a computer processor determines whether the data subject access request is subject to fulfillment constraints. If so, then the computer processor notifies the requestor that the data subject access request is subject to one or more limitations and the computer processor takes action based on those limitations. Fulfillment constraint data is updated and maintained in a database or server.
In particular embodiments, in response a data subject submitting a request to delete their personal data from an organization's systems, the system may: (1) automatically determine where the data subject's personal data is stored; and (2) in response to determining the location of the data (which may be on multiple computing systems), automatically facilitate the deletion of the data subject's personal data from the various systems (e.g., by automatically assigning a plurality of tasks to delete data across multiple business systems to effectively delete the data subject's personal data from the systems).
In various embodiments, a data subject request fulfilment system may be adapted to prioritize the processing of data subject access requests based on metadata of the data subject access request. For example, the system may be adapted for: (1) in response to receiving a data subject access request, obtaining metadata regarding the data subject; (2) using the metadata to determine whether a priority of the data subject access request should be adjusted based on the obtained metadata; and (3) in response to determining that the priority of the data subject access request should be adjusted based on the obtained metadata, adjusting the priority of the data subject access request.
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.
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.
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.
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.
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.
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.
A system for identifying and determining whether a particular cookie may include personal data, in any embodiment described herein, is configured to analyze collected cookies to determine whether the collected cookies may be used to directly or indirectly identify a particular individual. The system may, for example: (1) generate one or more virtual profiles; (2) use the one or more virtual profiles to access a plurality of websites; (3) collect cookie data for the plurality of websites for the one or more virtual profiles; and (4) analyze the cookie data to determine whether a particular website of the plurality of websites utilizes one or more cookies which may potentially include personal data. The system may then generate a report of the analysis, and display the report to an administrator or other individual associated with the particular website.
Data processing systems and methods, according to various embodiments are adapted for efficiently processing data to allow for the streamlined assessment of the risk level associated with particular privacy campaigns. The systems may provide a centralized repository of templates of privacy-related question/answer pairings for various vendors, products (e.g., software products), and services. Different entities may electronically access the templates (which may be periodically updated and centrally audited) and customize the templates for evaluating the risk associated with the entities' respective business endeavors that involve the relevant vendors, products, or services.
A privacy compliance oversight system, according to particular embodiments, is configured to facilitate review and oversight of privacy campaign information by a third-party regulator. The system may implement this oversight by: (1) flagging a particular privacy campaign, project, or other activity for review by a third-party regulators; (2) in response to flagging the particular privacy campaign, project, or other activity for review, preparing campaign data associated with the particular privacy campaign, project, or other activity for review by the third-party regulator; (3) providing the third party regulator with access to the privacy campaign data; (4) receiving one or more pieces of feedback associated with the particular privacy campaign, project, or other activity from the third-party regulators; and (5) in response to receiving the one or more pieces of feedback, modifying the privacy campaign data to include the one or more pieces of feedback.
Data processing systems and methods, according to various embodiments, perform privacy assessments and monitor new versions of computer code for updated features and conditions that relate to compliance with privacy standards. The systems and methods may obtain a copy of computer code (e.g., a software application or a website) that collects and/or uses personal data, and then automatically analyzes the computer code to identify one or more privacy-related attributes that may impact privacy assessment standards. In various embodiments, the system is adapted to monitor one or more locations (e.g., an online software application marketplace, and/or a specified website) to determine whether the application or website has changed. The system may, after analyzing the computer code, display the privacy-related attributes, collect information regarding the attributes, and automatically notify one or more designated individuals (e.g., privacy office representatives) regarding the attributes and information collected.
A privacy compliance monitoring system, according to particular embodiments, is configured to track a user's system inputs and responses to questions regarding a particular privacy campaign in order to monitor any potential abnormal or misleading response. In various embodiments, the system is configured to track changes to a user's responses, monitor an amount of time it takes a user to respond, determine a number of times that a user changes a response and/ take other actions to determine whether a particular response may be abnormal. In various embodiments, the system is configured to automatically flag one or more questions based on determining that the user may have provided an abnormal response.
Data processing systems and methods for receiving data regarding a plurality of data privacy campaigns and for using that data to modify stored data associated with the data privacy campaign. In various embodiments, the system may be adapted to: (1) display one or more visual summaries of one or more data flow diagrams that visually depicts key features of the data flow, such as whether data is confidential and/or encrypted; (2) automatically assess and display a relative risk associated with each campaign; and (3) automatically set, monitor, and facilitate the timely completion of an audit schedule for each campaign. In some embodiments, the system is configured to provide a mobile application via which a user may view information related to the privacy campaign, modify that data, etc.
Data processing computer systems, in various embodiments, are adapted for: (1) presenting a threshold privacy assessment that includes a first set of privacy-related questions for a privacy campaign; (2) receiving respective answers to the first set of questions; (3) using this initial set of answers to calculate an initial privacy risk score for the privacy campaign; (4) determining whether the privacy risk score exceeds the threshold privacy risk value; (5) in response to the privacy risk score exceeding the threshold privacy risk value, providing one or more supplemental questions to the user to facilitate the completion of a full privacy impact assessment. In some embodiments, in response to determining that the privacy risk score does not exceed the threshold privacy risk value, the systems and methods provide an indication that the particular privacy campaign is a relatively low privacy campaign.
Data processing systems and methods, according to various embodiments, are configured for generating personal data inventories for an organization by: (1) conducting, by one or more computer processors, privacy impact assessments for each of the organization's new business initiatives, the privacy impact assessments including both data-mapping and non-data-mapping questions; (2) flagging, by one or more computer processors, any data-mapping questions within the privacy impact assessments as data mapping questions; and (3) generating, one or more computer processors, personal data inventories on-demand based on the flagged data-mapping data.
G06F 7/04 - Identity comparison, i.e. for like or unlike values
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
H04L 29/06 - Communication control; Communication processing characterised by a protocol
52.
DATA PROCESSING SYSTEMS AND METHODS FOR INTEGRATING PRIVACY INFORMATION MANAGEMENT SYSTEMS WITH DATA LOSS PREVENTION TOOLS OR OTHER TOOLS FOR PRIVACY DESIGN
Computer implemented methods, according to various embodiments, comprise: (1) integrating a privacy management system with DLP tools; (2) using the DLP tools to identify sensitive information that is stored in computer memory outside of the context of the privacy management system; and (3) in response to the sensitive data being discovered by the DLP tool, displaying each area of sensitive data to a privacy officer (e.g., similar to pending transactions in a checking account that have not been reconciled). A designated privacy officer may then select a particular entry and either match it up (e.g., reconcile it) with an existing data flow or campaign in the privacy management system, or trigger a new privacy assessment to be done on the data to capture the related privacy attributes and data flow information.
A privacy compliance measurement system, according to particular embodiments, is configured to determine compliance with one or more privacy compliance requirements by an organization or sub-group of the organization. In various embodiments, the system is configured to determine a privacy maturity rating for each of a plurality of sub-groups within an organization. In some embodiments, the privacy maturity rating is based at least in part on: (1) a frequency of risks or issues identified with Privacy Impact Assessments (PIAs) performed or completed by the one or sub-groups; (2) a relative training level of members of the sub-groups with regard to privacy related matters; (3) a breadth and amount of personal data collected by the sub-groups; and/or (4) etc. In various embodiments, the system is configured to automatically modify one or more privacy campaigns based on the determined privacy maturity ratings.
In various embodiments, a data map generation system is configured to: (1) enable a user to specify one or more criteria; (2) identify one or more data flows based at least in part on the one or more specified criteria; (3) generate a data map based at least in part on the identified one or more data flows; and (4) display the data map to any suitable individual (e.g., the user). In particular embodiments, the system is configured to display all data flows associated with a particular organization that are stored within the system. In other embodiments, the system is configured to display all data flows that are associated with a particular privacy campaign undertaken by the organization. In other embodiments, the system is configured to generate and/or display one or more data maps that include a particular attribute that make up one or more data flows.
In various embodiments, a privacy campaign data modification system is configured to store electronic messages in memory and associate those electronic messages with a particular privacy campaign. In particular, a privacy officer or other individual may receive e-mails or other electronic messages that are associated with an existing privacy campaign or an existing privacy impact assessment currently being performed for a particular privacy campaign. In various embodiments, it may be necessary to store and maintain the electronic messages for any suitable reason (e.g., record keeping, auditing, etc.).
DATA PROCESSING SYSTEMS AND COMMUNICATIONS SYSTEMS AND METHODS FOR INTEGRATING PRIVACY COMPLIANCE SYSTEMS WITH SOFTWARE DEVELOPMENT AND AGILE TOOLS FOR PRIVACY DESIGN
Data processing systems and methods for: (1) receiving, via privacy data compliance software, from a first set of users, respective answers for question/answer pairings regarding the proposed design of a product; (2) using the question/answer pairings to prepare an initial privacy impact assessment for the product; (3) displaying, via the privacy data compliance software, the plurality of question/answer pairings to a second set of users, and receiving recommended steps to be implemented, before the product's implementation date, as part of the design of the product; (4) initiating the generation of one or more tasks in project management software that would advance the completion of the recommended steps; and (5) after the tasks have been completed, generating, by the privacy data compliance software, an updated privacy impact assessment for the product that reflects the fact that the tasks have been completed.
Data processing systems and methods for: (1) receiving from a first set of users, respective answers for question/answer pairings regarding a product's proposed design; (2) using the question/answer pairings to prepare an initial privacy impact assessment for the product; (3) displaying the plurality of question/answer pairings to a second set of users; (4) receiving recommended steps to be implemented, before the product's implementation date, as part of the design of the product to address any privacy-related concerns identified in the initial privacy impact assessment; and (5) after the tasks have been completed, generating a report documenting that: (a) the initial privacy assessment has been conducted for the product; (b) one or more revisions have been made to the product to facilitate the compliance of the product with the one or more privacy standards; and (c) an updated privacy assessment has been conducted for the product.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
58.
DATA PROCESSING SYSTEMS AND METHODS FOR OPERATIONALIZING PRIVACY COMPLIANCE AND ASSESSING THE RISK OF VARIOUS RESPECTIVE PRIVACY CAMPAIGNS
Data processing systems and methods for retrieving data regarding a plurality of data privacy campaigns and for using that data to assess a relative risk associated with the data privacy campaign. In various embodiments, the system may be adapted to: (1) display one or more visual summaries of one or more data flow diagrams that visually depicts key features of the data flow, such as whether data is confidential and/or encrypted; (2) allow for multiple users to be assigned responsibility for populating different respective questions that are required to define the data flow; (3) automatically assess and display a relative risk associated with each campaign; and (4) automatically set, monitor, and facilitate the timely completion of an audit schedule for each campaign.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 17/30 - Information retrieval; Database structures therefor
G06F 19/28 - for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
Data processing systems and methods for retrieving data regarding a plurality of data privacy campaigns and for using that data to assess a relative risk associated with the data privacy campaign. In various embodiments, the system may be adapted to: (1) display one or more visual summaries of one or more data flow diagrams that visually depicts key features of the data flow, such as whether data is confidential and/or encrypted; (2) allow for multiple users to be assigned responsibility for populating different respective questions that are required to define the data flow; (3) automatically assess and display a relative risk associated with each campaign; and (4) automatically set, monitor, and facilitate the timely completion of an audit schedule for each campaign.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 99/00 - Subject matter not provided for in other groups of this subclass
60.
DATA PROCESSING SYSTEMS AND METHODS FOR EFFICIENTLY COMMUNICATING DATA FLOWS IN PRIVACY CAMPAIGNS
Data processing systems and methods for retrieving data regarding a plurality of data privacy campaigns and for using that data to assess a relative risk associated with the data privacy campaign. In various embodiments, the system may be adapted to: (1) display one or more visual summaries of one or more data flow diagrams that visually depicts key features of the data flow, such as whether data is confidential and/or encrypted; (2) allow for multiple users to be assigned responsibility for populating different respective questions that are required to define the data flow; (3) automatically assess and display a relative risk associated with each campaign; and (4) automatically set, monitor, and facilitate the timely completion of an audit schedule for each campaign.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 99/00 - Subject matter not provided for in other groups of this subclass
patents
