Goods & Services

Downloadable electronic publications in the nature of newsletters in the fields of artificial intelligence governance, data compliance, data privacy, data security, and data management. Training services in the fields of artificial intelligence governance, data compliance, data privacy, data security, and data management; educational services, namely, conducting workshops, seminars, conferences, non-downloadable webinars, multimedia presentations and trainings in the fields of artificial intelligence governance, data compliance, data privacy, data security, and data management. Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; software as a service (SAAS) services featuring software using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; platform as a service (PAAS) services featuring software using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; technological consultancy in the field of artificial intelligence (AI) technology, data compliance, data privacy, data security, data policy enforcement and data management; computer software consulting.

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing user access levels to computing software applications based on user state changes detected via integration with a third-party system. Specifically, the disclosed system determines a user data object representing a user state associated with a user account in connection with a group of users of an entity by integrating with a third-party user management system. The disclosed system determines application data objects representing computing software applications that correspond to the group of users. Based on the user state, the disclosed system modifies a user access level of the user data object for a computing software application corresponding to the group of users. The disclosed system can generate a request to modify the user access level for providing to an administrator client device or automatically modify the user access level via integration with the third-party user management system.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems according to detect and correct configuration gaps with specific system requirements frameworks. Specifically, the disclosed system accesses a digital data repository to determine attribute values of data objects representing functions or infrastructure associated with handling target data for an entity. The disclosed system determines a digital representation of a system requirements framework that indicates controls associated with handling specific data types. Based on the attribute values and a gap rules set associated with the system requirements framework, the disclosed system determines configuration gaps to be addressed via control actions for installing controls in connection with various data assets or data processing operations. The disclosed system generates tasks to display via a graphical user interface of a computing device for applying modifications to the data assets and/or data processing operations to address the configuration gaps.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 30/018 - Certifying business or products
• G06Q 30/0203 - Market surveysMarket polls

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for implementation of executing operations with one or more hardware processors to access a tenant database within a multi-tenant environment and generate mapping records for one or more user accounts and one or more objects by executing an eager evaluation model in response to creation of an access policy. The disclosed systems generate policy subject mappings mapping the access policy to user accounts based on attributes of the user account and policy object mappings mapping the access policy to objects according to the attributes of the objects. Upon receiving a request from a user account to access an object within the tenant database, the disclosed systems provide access to the one or more objects based on the policy subject mappings, the policy object mappings, and attributes of the user account.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for implementation of executing operations with one or more hardware processors to access a tenant database within a multi-tenant environment and generate mapping records for one or more user accounts and one or more objects by executing an eager evaluation model in response to creation of an access policy. The disclosed systems generate policy subject mappings mapping the access policy to user accounts based on attributes of the user account and policy object mappings mapping the access policy to objects according to the attributes of the objects. Upon receiving a request from a user account to access an object within the tenant database, the disclosed systems provide access to the one or more objects based on the policy subject mappings, the policy object mappings, and attributes of the user account.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a decision tree to generate a recommended action in response to detecting a change in a data map representing a computing environment. The disclosed system determines relationships among data objects representing digital data, digital assets, and data processing activities and generates a data map according to the relationships. The disclosed system monitors the data map for changes and, responsive to determining a change in the data map, traverses a decision tree by executing one or more calls to one or more application programming interfaces according to the change in the data map and one or more data policies relevant to the changes. The disclosed systems utilize the decision tree and application programming interfaces to generate a recommended action for modifying digital assets, digital data, and/or data processing activities.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 10/10 - Office automationTime management

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a decision tree to generate a recommended action in response to detecting a change in a data map representing a computing environment. The disclosed system determines relationships among data objects representing digital data, digital assets, and data processing activities and generates a data map according to the relationships. The disclosed system monitors the data map for changes and, responsive to determining a change in the data map, traverses a decision tree by executing one or more calls to one or more application programming interfaces according to the change in the data map and one or more data policies relevant to the changes. The disclosed systems utilize the decision tree and application programming interfaces to generate a recommended action for modifying digital assets, digital data, and/or data processing activities.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• H04L 41/0213 - Standardised network management protocols, e.g. simple network management protocol [SNMP]

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that scan digital applications to intelligently detect potentially insecure data access controls from the digital applications. In particular, the disclosed systems can parse application development libraries to identify potentially insecure data access controls available for utilization within digital applications. Moreover, the disclosed systems can scan an application to detect access controls utilized within the application. Additionally, the disclosed systems can compare the detected, utilized access controls of an application to the potentially insecure data access controls to determine one or more potentially insecure data access controls utilized within the scanned application. In addition, based on detecting one or more potentially insecure data access controls utilized within the scanned application, the disclosed systems can trigger a variety of digital actions within an application scanning platform for the application in response to the one or more potentially insecure data access controls.

IPC Classes  ?

• 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

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that scan digital applications to intelligently detect potentially insecure data access controls from the digital applications. In particular, the disclosed systems can parse application development libraries to identify potentially insecure data access controls available for utilization within digital applications. Moreover, the disclosed systems can scan an application to detect access controls utilized within the application. Additionally, the disclosed systems can compare tire detected, utilized access controls of an application to the potentially insecure data access controls to determine one or more potentially insecure data access controls utilized within the scanned application. In addition, based on detecting one or more potentially insecure data access controls utilized within the scanned application, the disclosed systems can trigger a variety of digital actions within an application scanning platform for the application in response to the one or more potentially insecure data access controls.

IPC Classes  ?

• 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 21/60 - Protecting data
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 67/00 - Network arrangements or protocols for supporting network services or applications

Abstract

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a repository of metadata-based recommendations to classify data sources using metadata from the data sources. For example, the disclosed systems can generate a repository of metadata-based recommendations that indicate recommended classifications for objects within data sources through metadata associated with a data source schema. In some instances, the disclosed systems identify metadata from a data source schema associated with the data source. Subsequently, the disclosed systems can match the identified metadata to a metadata-based recommendation via metadata mappings in the metadata-based recommendation repository to select a metadata-based recommendation. Furthermore, the disclosed systems can also utilize a classifier model to generate predicted labels for the data source and update the metadata-based recommendation repository with a mapping between the predicted labels and metadata corresponding to the data source schema of the data source.

IPC Classes  ?

• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/21 - Design, administration or maintenance of databases
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 20/20 - Ensemble learning

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Goods & Services

Downloadable electronic publications in the nature of newsletters and ebooks in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Digital media, namely, downloadable webcasts, podcasts, and multimedia files containing audio and video recordings in the fields of AI governance, privacy, consent, ethics, compliance, data, risk, and workplace culture; Downloadable chatbot software using artificial intelligence (AI) for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Downloadable software in the nature of a mobile application for managing, organizing, and facilitating events, conferences, and meetings in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Downloadable software development kits (SDK); Downloadable application programming interface (API) software Business consulting services in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Providing business information in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management Training services in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Educational services, namely, conducting workshops, seminars, conferences, non-downloadable webinars, multimedia presentations and trainings in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Providing a website featuring blogs and non-downloadable publications in the nature of articles in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Online publication of blogs, letters, newsletters, articles, news stories, and fact sheets in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Providing a non-downloadable multimedia program series featuring content about AI governance, privacy, consent, ethics, compliance, data, risk, and workplace culture via a video-on-demand service; Entertainment services, namely, providing podcasts and webcasts in the fields of AI governance, privacy, consent, ethics, compliance, data, risk, and workplace culture Providing temporary use of on-line non-downloadable software and applications using artificial intelligence for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; software as a service (SAAS) services featuring software using artificial intelligence for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; platform as a service (PAAS) services featuring software using artificial intelligence for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; consultancy in the field of regulatory compliance, artificial intelligence governance and technology, risk management, data privacy, data security, data policy enforcement, and data management; computer software consulting; Providing temporary use of online non-downloadable software and applications for managing user consent, cookies, and privacy preferences; Software as a service (SAAS) services for managing user consent, cookies, and privacy preferences; Technology consultation in the field of cybersecurity; Data encryption services; Providing an online database featuring regulatory information in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Application service provider featuring application programming interface (API) software; Providing temporary use of online non-downloadable software using artificial intelligence (AI) for automating workflows, record-keeping, reporting, and notification guidance; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management Consulting services in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; Providing regulatory compliance information in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Advisory consulting services relating to regulatory compliance matters in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management

Goods & Services

Downloadable electronic publications in the nature of newsletters and ebooks in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Digital media, namely, downloadable webcasts, podcasts, and multimedia files containing audio and video recordings in the fields of AI governance, privacy, consent, ethics, compliance, data, risk, and workplace culture; Downloadable chatbot software using artificial intelligence (AI) for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Downloadable software in the nature of a mobile application for managing, organizing, and facilitating events, conferences, and meetings in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Downloadable software development kits (SDK); Downloadable application programming interface (API) software Business consulting services in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Providing business information in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management Training services in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Educational services, namely, conducting workshops, seminars, conferences, non-downloadable webinars, multimedia presentations and trainings in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Providing a website featuring blogs and non-downloadable publications in the nature of articles in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Online publication of blogs, letters, newsletters, articles, news stories, and fact sheets in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Providing a non-downloadable multimedia program series featuring content about AI governance, privacy, consent, ethics, compliance, data, risk, and workplace culture via a video-on-demand service; Entertainment services, namely, providing podcasts and webcasts in the fields of AI governance, privacy, consent, ethics, compliance, data, risk, and workplace culture Providing temporary use of on-line non-downloadable software and applications using artificial intelligence for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; software as a service (SAAS) services featuring software using artificial intelligence for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; platform as a service (PAAS) services featuring software using artificial intelligence for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; consultancy in the field of regulatory compliance, artificial intelligence governance and technology, risk management, data privacy, data security, data policy enforcement, and data management; computer software consulting; Providing temporary use of online non-downloadable software and applications for managing user consent, cookies, and privacy preferences; Software as a service (SAAS) services for managing user consent, cookies, and privacy preferences; Technology consultation in the field of cybersecurity; Data encryption services; Providing an online database featuring regulatory information in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Application service provider featuring application programming interface (API) software; Providing temporary use of online non-downloadable software using artificial intelligence (AI) for automating workflows, record-keeping, reporting, and notification guidance; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management Consulting services in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, data policy enforcement, and data management; Providing regulatory compliance information in the field of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management; Advisory consulting services relating to regulatory compliance matters in the fields of regulatory compliance, artificial intelligence governance, risk management, data privacy, data security, and data management

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for dynamically generating and modifying interactive graph-based taxonomies associated with data processes in various domains. The disclosed system generates node data objects representing a domain-category hierarchy in connection with one or more computing data processes via a library of tools. The disclosed systems generates an attribute data object corresponding to an attribute assigned to a node data object in the graph-based taxonomy and links the attribute data object to node data objects according to parent/child relationships of the hierarchy. The disclosed system utilizes the parent/child relationships of the node data objects and attribute data object to aggregate attribute values of the attributes according to one or more aggregation operations. The disclosed systems provide indications of the aggregated attribute values for display via a graphical user interface for use in performing data processes.

IPC Classes  ?

• G06F 16/901 - IndexingData structures thereforStorage structures
• G06F 16/904 - BrowsingVisualisation therefor

Abstract

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for capturing, analyzing, and classifying network traffic data associated with a network communication between computing systems. For example, the disclosed systems execute operations to generate classifications of the content of the network traffic data utilizing a classification model. For example, the disclosed systems determine risk levels based on whether network traffic data transmitted within the transport layer of the network traffic data adheres to the requirements of the data policy. In certain aspects, the disclosed systems analyze captured network traffic data based a live network transmissions, logged network transmissions, an API specification, and/or API endpoints. In some aspects, the disclosed systems provide a classification analysis including risk levels associated with the network traffic data via a custom graphical user interface.

IPC Classes  ?

• G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
• 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
• G06N 3/02 - Neural networks

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for capturing, analyzing, and classifying network traffic data associated with a network communication between computing systems. For example, the disclosed systems execute operations to generate classifications of the content of the network traffic data utilizing a classification model. For example, the disclosed systems determine risk levels based on whether network traffic data transmitted within the transport layer of the network traffic data adheres to the requirements of the data policy. In certain aspects, the disclosed systems analyze captured network traffic data based a live network transmissions, logged network transmissions, an API specification, and/or API endpoints. In some aspects, the disclosed systems provide a classification analysis including risk levels associated with the network traffic data via a custom graphical user interface.

IPC Classes  ?

• H04L 43/045 - Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
• H04L 9/40 - Network security protocols
• H04L 43/026 - Capturing of monitoring data using flow identification

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems to classify and modify digital content items to satisfy digital data requirements of data policies. For example, the content classification system validates, enforces, and remediates digital data content corresponding to digital data requirements of a data policy based on data types covered by the data policy. The disclosed systems generate classifications for digital content items by accessing digital content items and generating mappings between the digital content items and a data policy. The disclosed systems utilize the mappings and digital data requirements of the data policy to determine whether the digital content items violate one or more elements of the data policy. The disclosed systems can perform various downstream operations to remediate the data policy violations, such as by causing various computing devices to modify the violating digital content items.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for detecting missing data in a digital data repository according to a set of digital data requirements and extracted data attributes for correcting database operations. The disclosed system utilizes a classifier model to classify digital content items via an integration with the digital data repository. The disclosed system generates mappings indicating that the digital content items correspond to digital data requirements of a data policy based on the classifications. The disclosed system utilizes the mappings to determine that one or more types of data are missing from the digital data repository as indicated by the digital data requirements. The disclosed system generate an indication of the data missing from the digital data repository for use in performing additional operations, such as modifying a database operation having access to the digital content items to prevent further errors.

IPC Classes  ?

• G06F 16/23 - Updating
• G06F 16/906 - ClusteringClassification

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for correcting entity detection errors with entity correction and resolution in optical character recognition for digitization of physical documents. Specifically, the disclosed system utilizes named entity recognition to extract entities from character strings (e.g., words) in a digital text document. The disclosed system also tokenizes the character strings in the digital text document based on attributes of the character strings. Furthermore, the disclosed system compares the extracted entities and tokenized character strings to determine similarity metrics between the extracted entities and tokenized character strings. The disclosed system also compares extracted entities to character strings including special/numerical characters to determine similarity metrics indicating correlation probabilities between entities and character strings. The disclosed systems generate mappings between the tokens and entities based on the similarity metrics to resolve entities to likely corresponding character strings while correcting for errors during entity extraction.

IPC Classes  ?

• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G06F 40/295 - Named entity recognition
• G06V 30/32 - Digital ink

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for optimizing computer memory usage for lookup lists in computer memory via probabilistic data structures. For example, the disclosed system generates a probabilistic data structure (e.g., a Bloom filter) to represent data in a lookup list including multi-token items by hashing items of the lookup list to sets of bit values in a bit vector. The disclosed system classifies text content in a digital document by utilizing a maximum number of tokens from multi-token items in the lookup list to select and compare sets of sequential tokens in the digital document to the probabilistic data structure. The disclosed system also iteratively reduces the number of tokens in sets of sequential tokens for subsequent comparisons. Furthermore, in some aspects, the disclosed system causes a computing device to modify a digital document and/or database operations based on the classifications.

IPC Classes  ?

• G06F 16/906 - ClusteringClassification
• G06F 16/901 - IndexingData structures thereforStorage structures
• G06F 16/93 - Document management systems

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for controlling access to artificial intelligence systems based on determined intent of queries. The disclosed system utilizes one or more digital content analysis models to determine an intent of one or more queries to an artificial intelligence system. The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system. Additionally, the disclosed system determines whether the intent of the one or more queries aligns with the intended use of the artificial intelligence system by generating a similarity score and comparing the similarity score to a similarity threshold. Based on whether the intent aligns with the intended use, the disclosed system executes computing instructions to enable or block the one or more queries from being processed by the artificial intelligence system.

IPC Classes  ?

• G06F 16/903 - Querying

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for controlling access to artificial intelligence systems based on determined intent of queries. The disclosed system utilizes one or more digital content analysis models to determine an intent of one or more queries to an artificial intelligence system. The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system. Additionally, the disclosed system determines whether the intent of the one or more queries aligns with the intended use of the artificial intelligence system by generating a similarity score and comparing the similarity score to a similarity threshold. Based on whether the intent aligns with the intended use, the disclosed system executes computing instructions to enable or block the one or more queries from being processed by the artificial intelligence system.

IPC Classes  ?

• G06F 16/332 - Query formulation

Abstract

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.

IPC Classes  ?

• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06F 18/241 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that scans application codes to detect data processing activity components utilized a type-based analysis. For example, the disclosed systems can extract data type information from input application code and utilize the data type information to identify a list of potential (or candidate) function call components for the particular extracted data type. In addition, the disclosed systems can utilize a pattern matching model to match the list of potential function call components to function call component signatures within the application code. Moreover, the disclosed systems can utilize the determined function call component signatures with a detector specification to identify particular data processing activity components (e.g., SDKs, targets, method calls) corresponding to the application code. Moreover, the disclosed systems can display the identified data processing activity components within a software profile for the application code.

IPC Classes  ?

• G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
• 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

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that scans application codes to detect data processing activity components utilized a type-based analysis. For example, the disclosed systems can extract data type information from input application code and utilize the data type information to identify a list of potential (or candidate) function call components for the particular extracted data type. In addition, the disclosed systems can utilize a pattern matching model to match the list of potential function call components to function call component signatures within the application code. Moreover, the disclosed systems can utilize the determined function call component signatures with a detector specification to identify particular data processing activity components (e.g., SDKs, targets, method calls) corresponding to the application code. Moreover, the disclosed systems can display the identified data processing activity components within a software profile for the application code.

IPC Classes  ?

• G06F 8/75 - Structural analysis for program understanding
• 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 8/74 - Reverse engineeringExtracting design information from source code
• G06F 8/41 - Compilation

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems to comply with system requirements frameworks that indicate specific requirements on how the computing systems should handle certain data types. In response to detecting a change to one or more digital data requirements of a system requirements framework, the disclosed systems access a configuration profile of an entity and utilize a large language model to determine if data assets or data processing operations comply with the changes to the digital data requirements. In response to determining a configuration gap of the configuration profile based on detected changes to the digital data requirements, the disclosed systems utilize a large language model to generate tasks for correcting the configuration gap. The disclosed systems generate one or more tasks by modifying data assets, data processing operations, or other digital data associated with the entity.

IPC Classes  ?

• G06Q 40/06 - Asset managementFinancial planning or analysis

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for facilitating digital report generation across separate software computing applications via application and digital data extraction integration. The disclosed system utilizes integrated software computing applications to perform cross-application tasks to generate and edit digital reports. The disclosed system provides tools within a data management software application to create a digital report from data objects generated from data extracted from digital data repositories associated with an entity. In response to a request via the data management software application, the disclosed system performs operations to launch a digital content editing application displaying a digital report template including the data represented by the data objects. In response to launching the digital content editing application, the disclosed system causes the digital content editing application to open a panel with tools corresponding to the data management software application for editing the digital report template.

IPC Classes  ?

• G06F 40/186 - Templates
• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 40/103 - Formatting, i.e. changing of presentation of documents
• G06V 30/416 - Extracting the logical structure, e.g. chapters, sections or page numbersIdentifying elements of the document, e.g. authors
• G06V 30/42 - Document-oriented image-based pattern recognition based on the type of document

Abstract

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.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 15/76 - Architectures of general purpose stored program computers
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06F 40/186 - Templates
• G06N 20/00 - Machine learning
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• H04L 9/40 - Network security protocols
• H04L 67/306 - User profiles
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

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 R 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.

IPC Classes  ?

• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 10/067 - Enterprise or organisation modelling

Abstract

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.

IPC Classes  ?

• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 50/26 - Government or public services

Abstract

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.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

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.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06Q 10/063 - Operations research, analysis or management

Goods & Services

Training services in the fields of artificial intelligence governance, data compliance, data privacy, data security, and data management; educational services, namely, conducting workshops, seminars, conferences, non-downloadable webinars, multimedia presentations and trainings in the fields of artificial intelligence governance, data compliance, data privacy, data security, and data management Providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; software as a service (SAAS) services featuring software using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; software as a service (SAAS) services featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; platform as a service (PAAS) services featuring software using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; platform as a service (PAAS) services featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for data compliance, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) for analytics-based machine learning, data automation, data and compliance generation and predictive analytics being business data analysis in the fields of data compliance, data privacy, data security, and data management; artificial intelligence as a service (AIAAS) featuring software using artificial intelligence (AI) to track compliance and monitor regulations and laws in the field of artificial intelligence governance, data compliance, data privacy, data security, data policy enforcement and data management; consultancy in the field of artificial intelligence (AI) technology, data compliance, data privacy, data security, data policy enforcement and data management; computer software consulting

Abstract

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.

IPC Classes  ?

• G06F 16/242 - Query formulation
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 16/23 - Updating
• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06N 20/00 - Machine learning

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for facilitating modification of components of artificial intelligence computing applications via aggregated risk scores of individual artificial intelligence computing application components. The disclosed system generates data objects representing the artificial intelligence computing application components. The disclosed system determines mappings between the data objects based on relationships of the artificial intelligence computing application components. Furthermore, the disclosed system generates risk scores for the data objects representing the components of the artificial intelligence computing application by administering risk assessments that correspond to a system requirements framework. The disclosed system also generates an interactive aggregated risk indicator indicating contributions of the components to the artificial intelligence computing application in relation to the system requirements framework by combining the risk scores of the data objects according to the mappings between the data objects.

IPC Classes  ?

• 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

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 11/30 - Monitoring
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 21/60 - Protecting data

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems to classify and modify digital content items to satisfy digital data requirements of data policies. For example, the content classification system validates, enforces, and remediates digital data content corresponding to digital data requirements of a data policy based on data types covered by the data policy. The disclosed systems generate classifications for digital content items by accessing digital content items and generating mappings between the digital content items and a data policy. The disclosed systems utilize the mappings and digital data requirements of the data policy to determine whether the digital content items violate one or more elements of the data policy. The disclosed systems can perform various downstream operations to remediate the data policy violations, such as by causing various computing devices to modify the violating digital content items.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

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.

IPC Classes  ?

• G06Q 30/018 - Certifying business or products
• G06Q 50/18 - Legal services
• H04L 9/40 - Network security protocols

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for dynamically generating and modifying interactive graph-based taxonomies associated with data processes in various domains. The disclosed system generates node data objects representing a domain-category hierarchy in connection with one or more computing data processes via a library of tools. The disclosed systems generates an attribute data object corresponding to an attribute assigned to a node data object in the graph-based taxonomy and links the attribute data object to node data objects according to parent/child relationships of the hierarchy. The disclosed system utilizes the parent/child relationships of the node data objects and attribute data object to aggregate attribute values of the attributes according to one or more aggregation operations. The disclosed systems provide indications of the aggregated attribute values for display via a graphical user interface for use in performing data processes.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/901 - IndexingData structures thereforStorage structures
• G06F 16/904 - BrowsingVisualisation therefor

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for detecting missing data in a digital data repository according to a set of digital data requirements and extracted data attributes for correcting database operations. The disclosed system utilizes a classifier model to classify digital content items via an integration with the digital data repository. The disclosed system generates mappings indicating that the digital content items correspond to digital data requirements of a data policy based on the classifications. The disclosed system utilizes the mappings to determine that one or more types of data are missing from the digital data repository as indicated by the digital data requirements. The disclosed system generate an indication of the data missing from the digital data repository for use in performing additional operations, such as modifying a database operation having access to the digital content items to prevent further errors.

IPC Classes  ?

• G06F 16/23 - Updating
• G06F 16/906 - ClusteringClassification

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for optimizing computer memory usage for lookup lists in computer memory via probabilistic data structures. For example, the disclosed system generates a probabilistic data structure (e.g., a Bloom filter) to represent data in a lookup list including multi-token items by hashing items of the lookup list to sets of bit values in a bit vector. The disclosed system classifies text content in a digital document by utilizing a maximum number of tokens from multi-token items in the lookup list to select and compare sets of sequential tokens in the digital document to the probabilistic data structure. The disclosed system also iteratively reduces the number of tokens in sets of sequential tokens for subsequent comparisons. Furthermore, in some aspects, the disclosed system causes a computing device to modify a digital document and/or database operations based on the classifications.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/901 - IndexingData structures thereforStorage structures
• G06F 16/906 - ClusteringClassification
• G06F 16/93 - Document management systems

Abstract

Techniques are disclosed for usage-tracking of various information security (InfoSec) entities for tenants/organization onboarded on an instant multi-tenant security assurance platform. The InfoSec entities include policies, procedures, controls and evidence tasks. A policy or procedure is enforced by implementing one or more controls, and the collection of one or more evidence tasks proves/verifies the implementation of a control. The InfoSec entities are linked to each other across the platform and accrue a number of benefits for the tenants. These include generating a security questionnaire response (SQR), defining a readiness project and an audit project, sharing InfoSec entities encompassing the various products of a tenant, automating risk assessment, automatic collection of evidence tasks for verifying the implementation and/or operational state/status of various mitigating controls, etc.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

In various embodiments, an entity may provide a WebView where a transaction between an entity and a data subject may be performed. As described herein, the transaction may involve the collection or processing of personal data associated with the data subject by the entity as part of a processing activity undertaken by the entity that the data subject is consenting to as part of the transaction. Additionally, the entity may provide a native application where the transactions between the entity and a data subject may be performed. In some embodiments, the system may be configured to share consent data between the WebView and the native application so data subjects experience a seamless transition while using either the WebView or the native application, and the data subjects are not required to go through a consent workflow for each of the WebView and the native application.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

Various aspects of the disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for facilitating the exchange of data among a diverse group of first and third party computing environments. Accordingly, various aspects of the disclosure provide a data exchange computing platform that facilitates data exchange among a diverse group of first and third party computing environments. In some aspects, the data exchange computing platform provides a data exchange service available to various first and third parties who wish to exchange data.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/40 - Network security protocols
• H04L 67/306 - User profiles

Abstract

Various aspects of the disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for facilitating the exchange of data among a diverse group of first and third party computing environments. Accordingly, various aspects of the disclosure provide a data exchange computing platform that facilitates data exchange among a diverse group of first and third party computing environments. In some aspects, the data exchange computing platform provides a data exchange service available to various first and third parties who wish to exchange data.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/40 - Network security protocols
• H04L 67/306 - User profiles

Abstract

Various aspects of the disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for facilitating the exchange of data among a diverse group of first and third party computing environments. Accordingly, various aspects of the disclosure provide a data exchange computing platform that facilitates data exchange among a diverse group of first and third party computing environments. In some aspects, the data exchange computing platform provides a data exchange service available to various first and third parties who wish to exchange data.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/40 - Network security protocols
• H04L 67/306 - User profiles

Abstract

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.

IPC Classes  ?

• G06Q 30/0203 - Market surveysMarket polls

Abstract

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.

IPC Classes  ?

• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06F 15/76 - Architectures of general purpose stored program computers
• G06F 16/95 - Retrieval from the web
• 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
• G06Q 10/067 - Enterprise or organisation modelling

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems according to detect and correct configuration gaps with specific system requirements frameworks. Specifically, the disclosed system accesses a digital data repository to determine attribute values of data objects representing functions or infrastructure associated with handling target data for an entity. The disclosed system determines a digital representation of a system requirements framework that indicates controls associated with handling specific data types. Based on the attribute values and a gap rules set associated with the system requirements framework, the disclosed system determines configuration gaps to be addressed via control actions for installing controls in connection with various data assets or data processes. The disclosed system generates tasks to display via a graphical user interface of a computing device for applying modifications to the data assets and/or data processes to address the configuration gaps.

IPC Classes  ?

• G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
• 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
• H04L 9/40 - Network security protocols

Abstract

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.

IPC Classes  ?

• G06F 9/54 - Interprogram communication

Abstract

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for managing user access levels to computing software applications based on user state changes detected via integration with a third-party system. Specifically, the disclosed system determines a user data object representing a user state associated with a user account in connection with a group of users of an entity by integrating with a third-party user management system. The disclosed system determines application data objects representing computing software applications that correspond to the group of users. Based on the user state, the disclosed system modifies a user access level of the user data object for a computing software application corresponding to the group of users. The disclosed system can generate a request to modify the user access level for providing to an administrator client device or automatically modify the user access level via integration with the third-party user management system.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for determining clusters of similar digital documents using unique document signatures. Specifically, the disclosed system processes digital text in a digital document to tokenize character strings (e.g., words) in the digital document by combining a subset of character values and string lengths in the character strings. Additionally, the disclosed system generates a document signature for the digital document by combining subsets of tokens generated for the digital document into a token sequence indicative of the digital text in the digital document. The disclosed system determines a cluster of similar digital documents including the digital document by comparing the document signature of the digital document to document signatures corresponding to a plurality of digital documents.

IPC Classes  ?

• G06F 16/31 - IndexingData structures thereforStorage structures
• G06F 16/35 - ClusteringClassification

Abstract

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.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Goods & Services

(Based on Use in Commerce) Arranging and conducting workshops, seminars, and training in the field of artificial intelligence governance; Educational training of others in the field of artificial intelligence governance; (Based on Intent To Use) ; Arranging and conducting conferences, non-downloadable webinars, and multimedia presentations in the field of artificial intelligence governance; Providing a website featuring blogs and non-downloadable publications in the nature of articles in the field of artificial intelligence governance; Online publication of blogs, letters, newsletters, articles, news stories, and fact sheets in the fields of artificial intelligence governance Providing information in the field of artificial intelligence governance law; Providing information in the field of artificial intelligence governance law via a website

Abstract

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.

IPC Classes  ?

• G06F 16/95 - Retrieval from the web
• G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
• G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Goods & Services

Professional business networking services; promoting the exchange of information and resources concerning corporate compliance, corporate ethics, and workplace misconduct; promoting public awareness of developments in the fields of corporate compliance, corporate ethics, and workplace misconduct. Arranging and conducting workshops, seminars, conferences, non-downloadable webinars, multimedia presentations and trainings in the fields of corporate compliance, corporate ethics, and workplace misconduct; Educational training of others in the fields of corporate compliance, corporate ethics, and workplace misconduct; Providing a website featuring blogs and non-downloadable publications in the nature of articles in the fields of corporate compliance, corporate ethics, and workplace misconduct; Online publication of blogs, letters, newsletters, articles, news stories, and fact sheets in the fields of corporate compliance, corporate ethics, and workplace misconduct.

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that generate insightful user interfaces that display data processing activity components from the application codes, data categories for the detected components, and/or modifications of data categories and/or data processing activity components. For example, the disclosed system can utilize the application code scan to categorize one or more data types and/or data processing purposes represented by the various detected data processing activity components. Additionally, the disclosed systems can generate dynamic graphical user interfaces with the data processing activity components and data categories to enable quick and insightful access to a wide breadth of information from an application code scan. Moreover, the disclosed systems can also determine (and display) changes of data processing activity components and/or data categories detected between scans of different versions of the application code.

IPC Classes  ?

• G06F 8/75 - Structural analysis for program understanding

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that generate insightful user interfaces that display data processing activity components from the application codes, data categories for the detected components, and/or modifications of data categories and/or data processing activity components. For example, the disclosed system can utilize the application code scan to categorize one or more data types and/or data processing purposes represented by the various detected data processing activity components. Additionally, the disclosed systems can generate dynamic graphical user interfaces with the data processing activity components and data categories to enable quick and insightful access to a wide breadth of information from an application code scan. Moreover, the disclosed systems can also determine (and display) changes of data processing activity components and/or data categories detected between scans of different versions of the application code.

IPC Classes  ?

• G06F 8/75 - Structural analysis for program understanding

Abstract

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

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.

IPC Classes  ?

• 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 21/60 - Protecting data
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06N 20/00 - Machine learning
• G06V 30/24 - Character recognition characterised by the processing or recognition method

Abstract

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a repository of metadata-based recommendations to classify data sources using metadata from the data sources. For example, the disclosed systems can generate a repository of metadata-based recommendations that indicate recommended classifications for objects within data sources through metadata associated with a data source schema. In some instances, the disclosed systems identify metadata from a data source schema associated with the data source. Subsequently, the disclosed systems can match the identified metadata to a metadata-based recommendation via metadata mappings in the metadata-based recommendation repository to select a metadata-based recommendation. Furthermore, the disclosed systems can also utilize a classifier model to generate predicted labels for the data source and update the metadata-based recommendation repository with a mapping between the predicted labels and metadata corresponding to the data source schema of the data source.

IPC Classes  ?

• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/21 - Design, administration or maintenance of databases
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a repository of metadata-based recommendations to classify data sources using metadata from the data sources. For example, the disclosed systems can generate a repository of metadata-based recommendations that indicate recommended classifications for objects within data sources through metadata associated with a data source schema. In some instances, the disclosed systems identify metadata from a data source schema associated with the data source. Subsequently, the disclosed systems can match the identified metadata to a metadata-based recommendation via metadata mappings in the metadata-based recommendation repository to select a metadata-based recommendation. Furthermore, the disclosed systems can also utilize a classifier model to generate predicted labels for the data source and update the metadata-based recommendation repository with a mapping between the predicted labels and metadata corresponding to the data source schema of the data source.

IPC Classes  ?

• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for processing data for a subset of domains in parallel with publishing data to a tenant database for another subset of domains within a shared infrastructure. Specifically, the disclosed system assigns one or more partitions of an intermediate shared processing queue to a set of domains indicated by a scan request from a client device. The disclosed system extracts data from a subset of domains of the set of domains via the one or more partitions and publishes scan results of the subset of domains to the tenant database. Furthermore, the disclosed system extracts, in parallel with publishing the data of the subset of domains, additional data of an additional subset of domains via the one or more partitions of the intermediate shared processing queue.

IPC Classes  ?

• G06F 16/951 - IndexingWeb crawling techniques
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

This disclosure describes some aspects of systems, non-transitory computer-readable media, and computer-implemented methods that generate insightful user interfaces that display data processing activity components from the application codes, data categories for the detected components, and/or modifications of data categories and/or data processing activity components. For example, the disclosed system can utilize the application code scan to categorize one or more data types and/or data processing purposes represented by the various detected data processing activity components. Additionally, the disclosed systems can generate dynamic graphical user interfaces with the data processing activity components and data categories to enable quick and insightful access to a wide breadth of information from an application code scan. Moreover, the disclosed systems can also determine (and display) changes of data processing activity components and/or data categories detected between scans of different versions of the application code.

IPC Classes  ?

• G06F 8/71 - Version control Configuration management
• G06F 8/75 - Structural analysis for program understanding
• 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
• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• H04L 9/40 - Network security protocols

Abstract

In various embodiments, a data processing consent capture system may be configured to prompt the data subject to consent to one or more types of data processing (e.g., to provide a desired consent) in response to identifying particular cookies (e.g., or types of data processing) that a data subject has not consented to. The system may, for example, substantially automatically prompt the data subject to consent for one or more particular types of data processing in response to determining that the user (e.g., data subject) has requested that a website or other system perform one or more functions that are not possible without a particular type of consent from the data subject. The system may, for example, prompt the user to consent in time for a certain interaction with the website, application, etc.

IPC Classes  ?

• G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• G06F 16/951 - IndexingWeb crawling techniques
• G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
• G06F 40/174 - Form fillingMerging

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 20/20 - Ensemble learning

Goods & Services

Digital media in the form of downloadable webcasts, podcasts, audio and video files, for use on mobile devices in the fields of responsible artificial intelligence (AI), AI governance, privacy management, consent management, compliance, ethics, environmental sustainability and responsibility, carbon accounting, reduction and offsetting, environmental, social, and corporate governance (ESG), and ESG program management. Entertainment services, namely, providing podcasts and webcasts in the fields of responsible artificial intelligence (AI), privacy management, consent management, compliance, AI governance, ethics, environmental sustainability and responsibility, carbon accounting, reduction and offsetting, environmental, social, and corporate governance (ESG) and ESG program management.

Goods & Services

(1) Digital media in the form of downloadable webcasts, podcasts, audio and video files, for use on mobile devices in the fields of responsible artificial intelligence (AI), AI governance, data privacy management, consumer consent management, compliance with privacy legislation, environmental sustainability and responsibility, carbon accounting, reduction and offsetting, environmental, social, and corporate governance (ESG), and environmental, social, and corporate governance (ESG) program management (1) Entertainment services, namely, providing podcasts and webcasts in the fields of responsible artificial intelligence (AI), data privacy management, consumer consent management, compliance with privacy legislation, AI governance, environmental sustainability and responsibility, carbon accounting, reduction and offsetting, environmental, social, and corporate governance (ESG) and environmental, social, and corporate governance (ESG) program management

Abstract

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 21/60 - Protecting data

Abstract

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.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

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.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/35 - ClusteringClassification

Abstract

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.

IPC Classes  ?

• G06F 16/242 - Query formulation
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 16/23 - Updating
• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06N 20/00 - Machine learning

Abstract

Techniques are disclosed for usage-tracking of various information security (InfoSec) entities for tenants/organization onboarded on an instant multi-tenant security assurance platform. The InfoSec entities include policies, procedures, controls and evidence tasks. A policy or procedure is enforced by implementing one or more controls, and the collection of one or more evidence tasks proves/verifies the implementation of a control. The InfoSec entities are linked to each other across the platform and accrue a number of benefits for the tenants. These include generating a security questionnaire response (SQR), defining a readiness project and an audit project, sharing InfoSec entities encompassing the various products of a tenant, automating risk assessment, automatic collection of evidence tasks for verifying the implementation and/or operational state/status of various mitigating controls, etc.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

Aspects of the present invention provide methods, systems, and/or the like for (1) receiving a set of delegates; (2) generating a corresponding GUI for each delegate in the set of delegates, wherein the corresponding GUI is configured with a respective display element that provides a request for the corresponding data unit and an input element for receiving the corresponding data unit; (3) generating at least one corresponding delegate record for each delegate of the set of delegates, wherein the at least one corresponding delegate record identifies the corresponding data unit and the corresponding graphical user interface and is stored in a centralized repository; (4) generating a corresponding electronic communication for each delegate of the set of delegates, wherein the corresponding electronic communication comprises a link to access the corresponding GUI; and (5) sending the corresponding electronic communication for each delegate of the set of delegates to the corresponding assignee.

IPC Classes  ?

• G06F 9/451 - Execution arrangements for user interfaces
• G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 9/44 - Arrangements for executing specific programs

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for determining clusters of similar digital documents using unique document signatures. Specifically, the disclosed system processes digital text in a digital document to tokenize character strings (e.g., words) in the digital document by combining a subset of character values and string lengths in the character strings. Additionally, the disclosed system generates a document signature for the digital document by combining subsets of tokens generated for the digital document into a token sequence indicative of the digital text in the digital document. The disclosed system determines a cluster of similar digital documents including the digital document by comparing the document signature of the digital document to document signatures corresponding to a plurality of digital documents.

IPC Classes  ?

• G06F 16/93 - Document management systems
• G06F 16/31 - IndexingData structures thereforStorage structures
• G06F 16/35 - ClusteringClassification
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

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.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

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.

IPC Classes  ?

• G06F 16/93 - Document management systems
• G06Q 10/00 - AdministrationManagement

Abstract

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.

IPC Classes  ?

• 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]
• G06N 3/045 - Combinations of networks
• G06N 3/0464 - Convolutional networks [CNN, ConvNet]

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing dynamic request queues to process electronic requests in a shared infrastructure environment. The disclosed system dynamically generates a plurality of separate request queues for tenant computing systems that utilize a shared processing infrastructure to issue electronic requests for processing by various recipient processors (e.g., one or more processing threads) by separating a primary request queue into the separate requests queues based on the tenant computing systems. The disclosed system also generates a plurality of queue order scores for the request queues based in part on a processing recency of each of the request queues and whether the request queues have pending electronic requests. The disclosed system processes electronic requests in the request queues by selecting a request queue based on the queue order scores and processing a batch of electronic requests utilizing a recipient processor.

IPC Classes  ?

• H04L 47/62 - Queue scheduling characterised by scheduling criteria
• H04L 67/1031 - Controlling of the operation of servers by a load balancer, e.g. adding or removing servers that serve requests
• H04L 47/52 - Queue scheduling by attributing bandwidth to queues
• H04L 47/60 - Queue scheduling implementing hierarchical scheduling

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• 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

Goods & Services

Digital media in the form of downloadable podcasts, multimedia files containing audio and video for use on mobile devices in the fields of responsible artificial intelligence (AI), AI governance, privacy management, consent management, compliance, ethics, environmental, social, and corporate governance (ESG), and ESG program management Entertainment services, namely, providing podcasts and webcasts in the fields of responsible artificial intelligence (AI), AI governance, environmental sustainability and responsibility, carbon accounting, reduction and offsetting

Goods & Services

Digital media in the form of downloadable webcasts, podcasts, multimedia files containing audio for use on mobile devices in the fields of privacy management, consent management, compliance, ethics, environmental, social, and corporate governance (ESG), and ESG program management Entertainment services, namely, providing podcasts and webcasts in the fields of privacy management, consent management, compliance, ethics, environmental, social, and corporate governance (ESG) and ESG program management

Abstract

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for managing implementation of data controls for computing systems. In various aspects, a method is provided that comprises: comparing a first version of a dataset describing a regulatory framework to a second version of the dataset to identify a change to a data control; in response: processing, using a featurization technique, a portion of the dataset to generate a feature representation of the change that comprises feature attributes representing the change; processing, using a first machine-learning model, the feature representation to generate tags representing characteristics of the change; processing, using a second machine-learning model, the tags to generate an applicable domain; identifying, based on the domain, a computing system affected by the change; and in response, coordinating an action to be performed for the computing system to address the change.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 40/40 - Processing or translation of natural language

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 11/30 - Monitoring
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 21/60 - Protecting data

Abstract

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.

IPC Classes  ?

• G05B 15/02 - Systems controlled by a computer electric
• G05B 23/02 - Electric testing or monitoring
• G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• G06F 40/40 - Processing or translation of natural language
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/045 - Combinations of networks
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 50/06 - Energy or water supply

Abstract

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.

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology
• G05B 15/02 - Systems controlled by a computer electric
• G05B 23/02 - Electric testing or monitoring
• G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• G06F 40/40 - Processing or translation of natural language
• G06N 3/045 - Combinations of networks
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 50/06 - Energy or water supply

Abstract

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.

IPC Classes  ?

• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/35 - ClusteringClassification

Abstract

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.

IPC Classes  ?

• G06F 16/24 - Querying
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06Q 10/063 - Operations research, analysis or management
• G06F 16/95 - Retrieval from the web

Abstract

A method, in various aspects, comprises: (1) receiving a query from a first party computing system related to integrating third party computing functionality into the first party computing system; (2) identifying a set of third party entities that provide the third party computing functionality; (3) accessing integration data; (4) identifying a set of reference entities, the set of reference entities including, for each respective third party entity, a respective reference entity that has previously integrated the third party computing into a respective reference entity computing system associated with the respective reference entity; (5) determining second integration data with respect to the set of reference entities integrating the third party computing functionality; (6) generating, based on the first integration data and the second integration data, data responsive to the query that is specific to the first party computing system; and (7) taking an action with respect to the data.

IPC Classes  ?

• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 10/067 - Enterprise or organisation modelling
• 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
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06F 15/76 - Architectures of general purpose stored program computers

Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for correcting entity detection errors with entity correction and resolution in optical character recognition for digitization of physical documents. Specifically, the disclosed system utilizes named entity recognition to extract entities from character strings (e.g., words) in a digital text document. The disclosed system also tokenizes the character strings in the digital text document based on attributes of the character strings. Furthermore, the disclosed system compares the extracted entities and tokenized character strings to determine similarity metrics between the extracted entities and tokenized character strings. The disclosed system also compares extracted entities to character strings including special/numerical characters to determine similarity metrics indicating correlation probabilities between entities and character strings. The disclosed systems generate mappings between the tokens and entities based on the similarity metrics to resolve entities to likely corresponding character strings while correcting for errors during entity extraction.

IPC Classes  ?

• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G06F 40/295 - Named entity recognition
• G06V 30/32 - Digital ink

Abstract

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.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 21/84 - Protecting input, output or interconnection devices output devices, e.g. displays or monitors

Abstract

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.

IPC Classes  ?

• G06Q 10/0635 - Risk analysis of enterprise or organisation activities

Abstract

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.

IPC Classes  ?

• G06Q 30/0203 - Market surveysMarket polls
• G06F 40/30 - Semantic analysis