WO2019023510A1 - Systèmes de traitement de données pour générer et charger un inventaire de données - Google Patents

Systèmes de traitement de données pour générer et charger un inventaire de données Download PDF

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Publication number
WO2019023510A1
WO2019023510A1 PCT/US2018/043976 US2018043976W WO2019023510A1 WO 2019023510 A1 WO2019023510 A1 WO 2019023510A1 US 2018043976 W US2018043976 W US 2018043976W WO 2019023510 A1 WO2019023510 A1 WO 2019023510A1
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WO
WIPO (PCT)
Prior art keywords
data
asset
inventory
computer
processors
Prior art date
Application number
PCT/US2018/043976
Other languages
English (en)
Inventor
Kabir A. Barday
Mihir S. Karanjkar
Steven W. Finch
Ken A. Browne
Nathan W. Heard
Aakash H. Patel
Jason L. Sabourin
Richard L. Daniel
Dylan D. Patton-Kuhl
Jonathan Blake Brannon
Original Assignee
OneTrust, LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/996,208 external-priority patent/US10181051B2/en
Priority claimed from US16/041,520 external-priority patent/US10275614B2/en
Priority claimed from US16/041,563 external-priority patent/US10282700B2/en
Priority claimed from US16/041,545 external-priority patent/US10204154B2/en
Priority claimed from US16/041,468 external-priority patent/US10284604B2/en
Application filed by OneTrust, LLC filed Critical OneTrust, LLC
Publication of WO2019023510A1 publication Critical patent/WO2019023510A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/105—Human resources

Definitions

  • Such personal data may include, but is not limited to, personally identifiable information (PII), which may be information that directly (or indirectly) identifies an individual or entity.
  • PII personally identifiable information
  • Examples of PII include names, addresses, dates of birth, social security numbers, and biometric identifiers such as a person's fingerprints or picture.
  • Other personal data may include, for example, customers' Internet browsing habits, purchase history, or even their preferences (e.g., likes and dislikes, as provided or obtained through social media).
  • a computer-implemented data processing method for generating a first data asset assessment comprises: (1) accessing, by one or more processors, a data model of a plurality of data assets used in the collection or storage of personal data, the data model defining one or more electronic links between the plurality of data assets and storing a plurality of data inventories that define a plurality of inventory attributes for each of the plurality of data assets; (2) identifying, by one or more processors, a first data inventory from the plurality of data inventories, each of the plurality of data inventories being associated with a respective data asset, wherein the first data inventory comprises a plurality of fields that each define a respective inventory attribute of a first data asset associated with the first data inventory; (3) determining, by one or more processors, for each of the plurality of fields, one or more populated inventory attributes and one or more unpopulated inventory attributes; (4) retrieving, by one or more processors, a data asset questionnaire template; (5) analyzing, by one or more processors, the data asset questionnaire
  • a computer-implemented data processing method for generating a processing activity template for creation of a processing activity assessment for feeding a plurality of activity attribute values in a processing activity inventory for a particular processing activity may comprise: (1) identifying, by one or more processors, for the particular processing activity, a plurality of processing activity attribute values; (2) electronically linking, by one or more processors, each respective processing activity attribute value of the plurality of processing activity attribute values to each of: (A) an associated respective question of a plurality of questions in the processing activity template; and (B) an associated respective field of a plurality of fields in in the processing activity inventory; (3) storing, by one or more processors, an indication of the electronic linking in computer memory; (4) creating, by one or more processors, the processing activity assessment based at least in part on the processing activity template, the processing activity assessment comprising the plurality of questions; (5) receiving, by one or more processors, from a user, one or more responses to the processing activity assessment comprising a respective response to each of the plurality of
  • a data processing data inventory generation system comprises: (1) one or more processors; (2) computer memory; and (3) a computer-readable medium storing computer-executable instructions.
  • the computer- executable instructions when executed by the one or more processors, cause the one or more processors to perform operations comprising: (1) identifying a primary data asset that collects or stores personal data of one or more data subjects; and (2) generating a data inventory for the primary data asset, the data inventory storing one or more primary data asset inventory attributes.
  • the one or more primary data asset inventory attributes comprise: (1) a type of personal data collected or stored by the primary data asset; and (2) primary transfer data associated with the personal data and the primary data asset.
  • the computer-executable instructions when executed by the one or more processors, further cause the one or more processors to perform operations comprising: (1) identifying a transfer data asset based at least in part on the primary transfer data; (2) modifying the data inventory to include the transfer data asset, the transfer data asset storing one or more transfer data asset inventory attributes comprising the primary transfer data; (3) digitally storing the data inventory in the computer memory; and (4) electronically linking the primary data asset to the transfer data asset in the data inventory.
  • a computer-implemented data processing method of generating a data inventory for a plurality of inter-related data assets utilized in the processing of one or more pieces of personal data comprises: (1) identifying, by one or more processors, from the plurality of inter-related data assets, a storage asset, the storage asset storing the one or more pieces of personal data collected from one or more data subjects; (2) identifying, by one or more processors, from the plurality of inter-related data assets, a collection asset that transfers the one or more pieces of personal data to the storage asset; (3) identifying, by one or more processors, from the plurality of inter-related data assets, a transfer asset to which the storage asset transfers the one or more pieces personal data; (4) digitally storing, by one or more processors, in computer memory, one or more storage asset inventory attributes comprising a type of personal data stored by the storage asset; (5) digitally storing, by one or more processors, in computer memory, one or more collection asset inventory attributes comprising the one or more pieces of personal data that the collection asset
  • generating the data inventory comprises: (1) associating the storage asset with the one or more storage asset inventory attributes in computer memory; (2) associating the collection asset with the one or more collection asset inventory attributes in computer memory; (3) associating the transfer asset with the one or more transfer asset inventory attributes in computer memory; (4) electronically linking the collection asset to the storage asset in computer memory; (5) electronically linking the storage asset to the transfer asset; and (6) electronically mapping the one or more pieces of personal data to the collection asset, the storage asset, and the transfer asset.
  • a computer-implemented data processing method for generating a data model of personal data processing activities comprises: (1) generating a data model for one or more data assets used in the collection or storage of personal data; (2) digitally storing the data model in computer memory; (3) identifying a first data asset of the one or more data assets; (4) modifying the data model to include the first data asset; (5) generating a data inventory for the first data asset in the data model; (6) associating the data inventory with the first data asset in computer memory; and (7) mapping the first data asset to at least one of the one or more data assets in the data model.
  • the data inventory comprises one or more inventory attributes such as, for example: (1) one or more processing activities associated with the first data asset; (2) transfer data associated with the first data asset; and (3) one or more pieces of personal data associated with the first asset.
  • FIG. 1 depicts a data model generation and population system according to particular embodiments.
  • FIG. 2 is a schematic diagram of a computer (such as the data model generation server 110, or data model population server 120) that is suitable for use in various embodiments of the data model generation and population system shown in FIG. 1.
  • a computer such as the data model generation server 110, or data model population server 120
  • FIG. 3 is a flowchart showing an example of steps performed by a Data Model Generation Module according to particular embodiments.
  • FIGS. 4-10 depict various exemplary visual representations of data models according to particular embodiments.
  • FIG. 11 is a flowchart showing an example of steps performed by a Data Model Population Module.
  • FIG. 12 is a flowchart showing an example of steps performed by a Data Population Questionnaire Generation Module.
  • FIG. 13 is a process flow for populating a data inventory according to a particular embodiment using one or more data mapping techniques.
  • FIGS. 14-25 depict exemplary screen displays and graphical user interfaces (GUIs) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., to configure a questionnaire for populating one or more inventory attributes for one or more data models, complete one or more assessments, etc.).
  • GUIs graphical user interfaces
  • FIG. 26 is a flowchart showing an example of steps performed by an Intelligent Identity Scanning Module.
  • FIG. 27 is schematic diagram of network architecture for an intelligent identity scanning system 2700 according to a particular embodiment.
  • FIG. 28 is a schematic diagram of an asset access methodology utilized by an intelligent identity scanning system 2700 in various embodiments of the system.
  • FIG. 29 is a flowchart showing an example of a processes performed by a Data Subject Access Request Fulfillment Module 2100 according to various embodiments.
  • FIGS. 30-31 depict exemplary screen displays and graphical user interfaces (GUIs) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., for the purpose of submitting a data subject access request or other suitable request).
  • GUIs graphical user interfaces
  • FIGS. 32-35 depict exemplary screen displays and graphical user interfaces (GUIs) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., for the purpose of flagging one or more risks associated with one or more particular questionnaire questions).
  • GUIs graphical user interfaces
  • a data model generation and population system is configured to generate a data model (e.g., one or more data models) that maps one or more relationships between and/or among a plurality of data assets utilized by a corporation or other entity (e.g., individual, organization, etc.) in the context, for example, of one or more business processes.
  • each of the plurality of data assets may include, for example, any entity that collects, processes, contains, and/or transfers data (e.g., such as a software application, "internet of things" computerized device, database, website, data- center, server, etc.).
  • a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.
  • the data model may store the following information: (1) the organization that owns and/or uses a particular data asset (a primary data asset, which is shown in the center of the data model in Figure 4); (2) one or more departments within the organization that are responsible for the data asset; (3) one or more software applications that collect data (e.g., personal data) for storage in and/or use by the data asset (e.g., or one or more other suitable collection assets from which the personal data that is collected, processed, stored, etc.
  • a primary data asset which is shown in the center of the data model in Figure 4
  • the data asset may store the following information: (1) the organization that owns and/or uses a particular data asset (a primary data asset, which is shown in the center of the data model in Figure 4); (2) one or more departments within the organization that are responsible for the data asset; (3) one or more software applications that collect data (e.g., personal data) for storage in and/or use by the data asset (e.g., or one or more other suitable collection assets from which the personal data that is collected, processed
  • the primary data asset is sourced); (4) one or more particular data subjects (or categories of data subjects) that information is collected from for use by the data asset; (5) one or more particular types of data that are collected by each of the particular applications for storage in and/or use by the data asset; (6) one or more individuals (e.g., particular individuals or types of individuals) that are permitted to access and/or use the data stored in, or used by, the data asset; (7) which particular types of data each of those individuals are allowed to access and use; and (8) one or more data assets (destination assets) that the data is transferred to for other use, and which particular data is transferred to each of those data assets.
  • the system may also optionally store information regarding, for example, which business processes and processing activities utilize the data asset.
  • the data model stores this information for each of a plurality of different data assets and may include links between, for example, a portion of the model that provides information for a first particular data asset and a second portion of the model that provides information for a second particular data asset.
  • the data model generation and population system may be implemented in the context of any suitable privacy management system that is configured to ensure compliance with one or more legal or industry standards related to the collection and/or storage of private information.
  • a particular organization, sub-group, or other entity may initiate a privacy campaign or other activity (e.g., processing activity) as part of its business activities.
  • the privacy campaign may include any undertaking by a particular organization (e.g., such as a project or other activity) that includes the collection, entry, and/or storage (e.g., in memory) of any personal data associated with one or more individuals.
  • a privacy campaign may include any project undertaken by an organization that includes the use of personal data, or any other activity that could have an impact on the privacy of one or more individuals.
  • personal data may include, for example: (1) the name of a particular data subject (which may be a particular individual); (2) the data subject's address; (3) the data subject's telephone number; (4) the data subject's e-mail address; (5) the data subject's social security number; (6) information associated with one or more of the data subject's credit accounts (e.g., credit card numbers); (7) banking information for the data subject; (8) location data for the data subject (e.g., their present or past location); (9) internet search history for the data subject; and/or (10) any other suitable personal information, such as other personal information discussed herein.
  • such personal data may include one or more cookies (e.g., where the individual is directly identifiable or may be identifiable based at least in part on information stored in the one or more cookies).
  • the system when generating a data model, may, for example: (1) identify one or more data assets associated with a particular organization; (2) generate a data inventory for each of the one or more data assets, where the data inventory comprises information such as: (a) one or more processing activities associated with each of the one or more data assets, (b) transfer data associated with each of the one or more data assets (data regarding which data is transferred to/from each of the data assets, and which data assets, or individuals, the data is received from and/or transferred to, (c) personal data associated with each of the one or more data assets (e.g., particular types of data collected, stored, processed, etc. by the one or more data assets), and/or (d) any other suitable information; and (3) populate the data model using one or more suitable techniques.
  • the data inventory comprises information such as: (a) one or more processing activities associated with each of the one or more data assets, (b) transfer data associated with each of the one or more data assets (data regarding which data is transferred to/from each of the data assets, and which data assets,
  • the one or more techniques for populating the data model may include, for example: (1) obtaining information for the data model by using one or more questionnaires associated with a particular privacy campaign, processing activity, etc. (e.g., using any suitable technique described herein); (2) using one or more intelligent identity scanning techniques discussed herein to identify personal data stored by the system and map such data to a suitable data model, data asset within a data model, etc. (e.g., using any suitable technique described herein); (3) obtaining information for the data model from a third-party application (or other application) using one or more application programming interfaces (API) (e.g., as described herein); and/or (4) using any other suitable technique or combination of techniques from any embodiment or embodiments described herein.
  • API application programming interfaces
  • the system is configured to generate and populate a data model substantially on the fly (e.g., as the system receives new data associated with particular processing activities).
  • the system is configured to generate and populate a data model based at least in part on existing information stored by the system (e.g., in one or more data assets), for example, using one or more suitable scanning techniques described herein.
  • a particular organization may undertake a plurality of different privacy campaigns, processing activities, etc. that involve the collection and storage of personal data.
  • each of the plurality of different processing activities may collect redundant data (e.g., may collect the same personal data for a particular individual more than once), and may store data and/or redundant data in one or more particular locations (e.g., on one or more different servers, in one or more different databases, etc.).
  • a particular organization may store personal data in a plurality of different locations which may include one or more known and/or unknown locations.
  • the system may be configured to create a data model that facilitates a straightforward retrieval of information stored by the organization as desired.
  • the system may be configured to use a data model in substantially automatically responding to one or more data access requests by an individual (e.g., or other organization).
  • data model generation and population may improve the functionality of an entity's computing systems by enabling a more streamlined retrieval of data from the system and eliminating redundant storage of identical data.
  • the present invention may be, for example, embodied as a computer system, a method, or a computer program product. Accordingly, various embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, particular embodiments may take the form of a computer program product stored on a computer-readable storage medium having computer-readable instructions (e.g., software) embodied in the storage medium. Various embodiments may take the form of web-implemented computer software. Any suitable computer-readable storage medium may be utilized including, for example, hard disks, compact disks, DVDs, optical storage devices, and/or magnetic storage devices.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner such that the instructions stored in the computer-readable memory produce an article of manufacture that is configured for implementing the function specified in the flowchart block or blocks.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.
  • blocks of the block diagrams and flowchart illustrations support combinations of mechanisms for performing the specified functions, combinations of steps for performing the specified functions, and program instructions for performing the specified functions. It should also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware-based computer systems that perform the specified functions or steps, or combinations of special purpose hardware and other hardware executing appropriate computer instructions.
  • FIG. 1 is a block diagram of a Data Model Generation and Population System 100 according to a particular embodiment.
  • the Data Model Generation and Population System 100 is part of a privacy compliance system (also referred to as a privacy management system), or other system, which may, for example, be associated with a particular organization and be configured to aid in compliance with one or more legal or industry regulations related to the collection and storage of personal data.
  • a privacy compliance system also referred to as a privacy management system
  • the Data Model Generation and Population System 100 is configured to: (1) generate a data model based on one or more identified data assets, where the data model includes a data inventory associated with each of the one or more identified data assets; (2) identify populated and unpopulated aspects of each data inventory; and (3) populate the unpopulated aspects of each data inventory using one or more techniques such as intelligent identity scanning, questionnaire response mapping, APIs, etc.
  • 100 includes one or more computer networks 115, a Data Model Generation Server 110, a Data Model Population Server 120, an Intelligent Identity Scanning Server 130, One or More Databases 140 or other data structures, one or more remote computing devices 150 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.), and One or More Third Party Servers 160.
  • the one or more computer networks 115 facilitate communication between the Data Model Generation Server 110, Data Model Population Server 120, Intelligent Identity Scanning Server 130, One or More Databases 140, one or more remote computing devices 150 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.), and One or More Third Party Servers 160.
  • the Data Model Generation Server 110 Data Model Population Server 120, Intelligent Identity Scanning Server 130, One or More Databases 140, one or more remote computing devices 150 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.), and One or More Third Party Servers 160 are shown as separate servers, it should be understood that in other embodiments, one or more of these servers and/or computing devices may comprise a single server, a plurality of servers, one or more cloud-based servers, or any other suitable configuration.
  • the one or more computer networks 115 may include any of a variety of types of wired or wireless computer networks such as the Internet, a private intranet, a public switch telephone network (PSTN), or any other type of network.
  • the communication link between The Intelligent Identity Scanning Server 130 and the One or More Third Party Servers 160 may be, for example, implemented via a Local Area Network (LAN) or via the Internet.
  • the One or More Databases 140 may be stored either fully or partially on any suitable server or combination of servers described herein.
  • FIG. 2 illustrates a diagrammatic representation of a computer 200 that can be used within the Data Model Generation and Population System 100, for example, as a client computer (e.g., one or more remote computing devices 130 shown in Figure 1), or as a server computer (e.g., Data Model Generation Server 110 shown in Figure 1).
  • the computer 200 may be suitable for use as a computer within the context of the Data Model Generation and Population System 100 that is configured to generate a data model and map one or more relationships between one or more pieces of data that make up the model.
  • the computer 200 may be connected (e.g., networked) to other computers in a LAN, an intranet, an extranet, and/or the Internet.
  • the computer 200 may operate in the capacity of a server or a client computer in a client-server network environment, or as a peer computer in a peer-to-peer (or distributed) network environment.
  • the Computer 200 may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, or any other computer capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that computer.
  • PC personal computer
  • PDA Personal Digital Assistant
  • STB set-top box
  • a switch or bridge any other computer capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that computer.
  • the term "computer” shall also be taken to include
  • An exemplary computer 200 includes a processing device 202, a main memory 204 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), static memory 206 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 218, which communicate with each other via a bus 232.
  • main memory 204 e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.
  • static memory 206 e.g., flash memory, static random access memory (SRAM), etc.
  • SRAM static random access memory
  • the processing device 202 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device 202 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLTvV) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets.
  • the processing device 202 may also be one or more special- purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like.
  • the processing device 202 may be configured to execute processing logic 226 for performing various operations and steps discussed herein.
  • the computer 120 may further include a network interface device 208.
  • the computer 200 also may include a video display unit 210 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 212 (e.g., a keyboard), a cursor control device 214 (e.g., a mouse), and a signal generation device 216 (e.g., a speaker).
  • a video display unit 210 e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)
  • an alphanumeric input device 212 e.g., a keyboard
  • a cursor control device 214 e.g., a mouse
  • a signal generation device 216 e.g., a speaker
  • the data storage device 218 may include a non-transitory computer-accessible storage medium 230 (also known as a non-transitory computer-readable storage medium or a non- transitory computer-readable medium) on which is stored one or more sets of instructions (e.g., software instructions 222) embodying any one or more of the methodologies or functions described herein.
  • the software instructions 222 may also reside, completely or at least partially, within main memory 204 and/or within processing device 202 during execution thereof by computer 200 - main memory 204 and processing device 202 also constituting computer- accessible storage media.
  • the software instructions 222 may further be transmitted or received over a network 1 15 via network interface device 208.
  • While the computer-accessible storage medium 230 is shown in an exemplary embodiment to be a single medium, the term “computer-accessible storage medium” should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
  • the term “computer-accessible storage medium” should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the computer and that cause the computer to perform any one or more of the methodologies of the present invention.
  • the term “computer-accessible storage medium” should accordingly be understood to include, but not be limited to, solid-state memories, optical and magnetic media, etc. Exemplary System Platform
  • a Data Model Generation and Population System 100 may be implemented in the context of any suitable system (e.g., a privacy compliance system).
  • the Data Model Generation and Population System 100 may be implemented to analyze a particular company or other organization's data assets to generate a data model for one or more processing activities, privacy campaigns, etc. undertaken by the organization.
  • the system may implement one or more modules in order to at least partially ensure compliance with one or more regulations (e.g., legal requirements) related to the collection and/or storage of personal data.
  • one or more regulations e.g., legal requirements
  • Various aspects of the system's functionality may be executed by certain system modules, including a Data Model Generation Module 300, Data Model Population Module 1100, Data Population Questionnaire Generation Module 1200, Intelligent Identity Scanning Module 2600, and Data Subject Access Request Fulfillment Module 2900. These modules are discussed in greater detail below.
  • the Data Model Generation Module 300, Data Model Population Module 1100, Data Population Questionnaire Generation Module 1200, Intelligent Identity Scanning Module 2600, and Data Subject Access Request Fulfillment Module 2900 may perform steps in addition to those described (e.g., such as one or more steps described with respect to one or more other modules, etc.).
  • system may be configured to perform any suitable combination of steps of any suitable steps described with respect to any of the modules discussed below.
  • Data Model Generation Module
  • a Data Model Generation Module 300 is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model.
  • a data asset e.g., data system, software application, etc.
  • a data asset may include, for example, any entity that collects, processes, contains, and/or transfers data (e.g., such as a software application, "internet of things" computerized device, database, website, data-center, server, etc.).
  • a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.
  • a particular data asset, or collection of data assets may be utilized as part of a particular data processing activity (e.g., direct deposit generation for payroll purposes).
  • a data model generation system may, on behalf of a particular organization (e.g., entity), generate a data model that encompasses a plurality of processing activities.
  • the system may be configured to generate a discrete data model for each of a plurality of processing activities undertaken by an organization.
  • the system begins, at Step 310, by generating a data model for one or more data assets and digitally storing the data model in computer memory.
  • the system may, for example, store the data model in the One or More Databases 140 described above (or any other suitable data structure).
  • generating the data model comprises generating a data structure that comprises information regarding one or more data assets, attributes and other elements that make up the data model.
  • the one or more data assets may include any data assets that may be related to one another.
  • the one or more data assets may be related by virtue of being associated with a particular entity (e.g., organization).
  • the one or more data assets may include one or more computer servers owned, operated, or utilized by the entity that at least temporarily store data sent, received, or otherwise processed by the particular entity.
  • the one or more data assets may comprise one or more third party assets which may, for example, send, receive and/or process personal data on behalf of the particular entity.
  • These one or more data assets may include, for example, one or more software applications (e.g., such as Expensify to collect expense information, QuickBooks to maintain and store salary information, etc.).
  • the system is configured to identify a first data asset of the one or more data assets.
  • the first data asset may include, for example, any entity (e.g., system) that collects, processes, contains, and/or transfers data (e.g., such as a software application, "internet of things" computerized device, database, website, data-center, server, etc.).
  • the first data asset may include any software or device utilized by a particular organization for such data collection, processing, transfer, etc.
  • the first data asset may be associated with a particular processing activity (e.g., the first data asset may make up at least a part of a data flow that relates to the collection, storage, transfer, access, use, etc.
  • the first data asset may clarify, for example, one or more relationships between and/or among one or more other data assets within a particular organization.
  • the first data asset may include a software application provided by a third party (e.g., a third party vendor) with which the particular entity interfaces for the purpose of collecting, storing, or otherwise processing personal data (e.g., personal data regarding customers, employees, potential customers, etc.).
  • the first data asset is a storage asset that may, for example: (1) receive one or more pieces of personal data form one or more collection assets; (2) transfer one or more pieces of personal data to one or more transfer assets; and/or (3) provide access to one or more pieces of personal data to one or more authorized individuals (e.g., one or more employees, managers, or other authorized individuals within a particular entity or organization).
  • the first data asset is a primary data asset associated with a particular processing activity around which the system is configured to build a data model associated with the particular processing activity.
  • the system is configured to identify the first data asset by scanning a plurality of computer systems associated with a particular entity (e.g., owned, operated, utilized, etc. by the particular entity).
  • the system is configured to identify the first data asset from a plurality of data assets identified in response to completion, by one or more users, of one or more questionnaires.
  • the system generates a first data inventory of the first data asset.
  • the data inventory may comprise, for example, one or more inventory attributes associated with the first data asset such as, for example: (1) one or more processing activities associated with the first data asset; (2) transfer data associated with the first data asset (e.g., how and where the data is being transferred to and/or from); (3) personal data associated with the first data asset (e.g., what type of personal data is collected and/or stored by the first data asset; how, and from where, the data is collected, etc.); (4) storage data associated with the personal data (e.g., whether the data is being stored, protected and deleted); and (5) any other suitable attribute related to the collection, use, and transfer of personal data.
  • inventory attributes associated with the first data asset such as, for example: (1) one or more processing activities associated with the first data asset; (2) transfer data associated with the first data asset (e.g., how and where the data is being transferred to and/or from); (3) personal data associated with the first data asset (e.g., what type of personal
  • the one or more inventory attributes may comprise one or more other pieces of information such as, for example: (1) the type of data being stored by the first data asset; (2) an amount of data stored by the first data asset; (3) whether the data is encrypted; (4) a location of the stored data (e.g., a physical location of one or more computer servers on which the data is stored); etc.
  • the one or more inventory attributes may comprise one or more pieces of information technology data related to the first data asset (e.g., such as one or more pieces of network and/or infrastructure information, IP address, MAC address, etc.).
  • the system may generate the data inventory based at least in part on the type of first data asset. For example, particular types of data assets may have particular default inventory attributes.
  • the system is configured to generate the data inventory for the first data asset, which may, for example, include one or more placeholder fields to be populated by the system at a later time. In this way, the system may, for example, identify particular inventory attributes for a particular data asset for which information and/or population of data is required as the system builds the data model.
  • the system may, when generating the data inventory for the first data asset, generate one or more placeholder fields that may include, for example: (1) the organization (e.g., entity) that owns and/or uses the first data asset (a primary data asset, which is shown in the center of the data model in Figure 4); (2) one or more departments within the organization that are responsible for the first data asset; (3) one or more software applications that collect data (e.g., personal data) for storage in and/or use by the first data asset (e.g., or one or more other suitable collection assets from which the personal data that is collected, processed, stored, etc.
  • the organization e.g., entity
  • the first data asset a primary data asset, which is shown in the center of the data model in Figure 4
  • the system may, when generating the data inventory for the first data asset, generate one or more placeholder fields that may include, for example: (1) the organization (e.g., entity) that owns and/or uses the first data asset (a primary data asset, which is shown in the center of the data model in Figure
  • the first data asset is sourced); (4) one or more particular data subjects (or categories of data subjects) that information is collected from for use by the first data asset; (5) one or more particular types of data that are collected by each of the particular applications for storage in and/or use by the first data asset; (6) one or more individuals (e.g., particular individuals or types of individuals) that are permitted to access and/or use the data stored in, or used by, the first data asset; (7) which particular types of data each of those individuals are allowed to access and use; and (8) one or more data assets (destination assets) that the data is transferred to from the first data asset, and which particular data is transferred to each of those data assets.
  • data assets destination assets
  • the system may be configured to generate the one or more placeholder fields based at least in part on, for example: (1) the type of the first data asset; (2) one or more third party vendors utilized by the particular organization; (3) a number of collection or storage assets typically associated with the type of the first data asset; and/or (4) any other suitable factor related to the first data asset, its one or more inventory attributes, etc.
  • the system may substantially automatically generate the one or more placeholders based at least in part on a hierarchy and/or organization of the entity for which the data model is being built. For example, a particular entity may have a marketing division, legal department, human resources department, engineering division, or other suitable combination of departments that make up an overall organization.
  • the system may identify that the first data asset will have both an associated organization and subdivision within the organization to which it is assigned.
  • the system may be configured to store an indication in computer memory that the first data asset is associated with an organization and a department within the organization.
  • the system modifies the data model to include the first data inventory and electronically links the first data inventory to the first data asset within the data model.
  • modifying the data model may include configuring the data model to store the data inventory in computer memory, and to digitally associate the data inventory with the first data asset in memory.
  • Figures 4 and 5 show a data model according to a particular embodiment.
  • the data model may store the following information for the first data asset: (1) the organization that owns and/or uses the first data asset; (2) one or more departments within the organization that are responsible for the first data asset; (3) one or more applications that collect data (e.g., personal data) for storage in and/or use by the first data asset; (4) one or more particular data subjects that information is collected from for use by the first data asset; (5) one or more collection assets from which the first asset receives data (e.g., personal data); (6) one or more particular types of data that are collected by each of the particular applications (e.g., collection assets) for storage in and/or use by the first data asset; (7) one or more individuals (e.g., particular individuals, types of individuals, or other parties) that are permitted to access and/or use the data stored in or used by the first data asset; (8) which particular types of data each of those individuals are allowed to access and use; and (9) one or more data assets (
  • the data model stores this information for each of a plurality of different data assets and may include one or more links between, for example, a portion of the model that provides information for a first particular data asset and a second portion of the model that provides information for a second particular data asset.
  • the system next identifies a second data asset from the one or more data assets.
  • the second data asset may include one of the one or more inventory attributes associated with the first data asset (e.g., the second data asset may include a collection asset associated with the first data asset, a destination asset or transfer asset associated with the first data asset, etc.).
  • a second data asset may be a primary data asset for a second processing activity, while the first data asset is the primary data asset for a first processing activity.
  • the second data asset may be a destination asset for the first data asset as part of the first processing activity.
  • the second data asset may then be associated with one or more second destination assets to which the second data asset transfers data.
  • particular data assets that make up the data model may define one or more connections that the data model is configured to map and store in memory.
  • the system is configured to identify one or more attributes associated with the second data asset, modify the data model to include the one or more attributes, and map the one or more attributes of the second data asset within the data model.
  • the system may, for example, generate a second data inventory for the second data asset that comprises any suitable attribute described with respect to the first data asset above.
  • the system may then modify the data model to include the one or more attributes and store the modified data model in memory.
  • the system may further, in various embodiments, associate the first and second data assets in memory as part of the data model.
  • the system may be configured to electronically link the first data asset with the second data asset.
  • such association may indicate a relationship between the first and second data assets in the context of the overall data model (e.g., because the first data asset may serve as a collection asset for the second data asset, etc.).
  • the system may be further configured to generate a visual representation of the data model.
  • the visual representation of the data model comprises a data map.
  • the visual representation may, for example, include the one or more data assets, one or more connections between the one or more data assets, the one or more inventory attributes, etc.
  • generating the visual representation (e.g., visual data map) of a particular data model may include, for example, generating a visual representation that includes: (1) a visual indication of a first data asset (e.g., a storage asset), a second data asset (e.g., a collection asset), and a third data asset (e.g., a transfer asset); (2) a visual indication of a flow of data (e.g., personal data) from the second data asset to the first data asset (e.g., from the collection asset to the storage asset); (3) a visual indication of a flow of data (e.g., personal data) from the first data asset to the third data asset (e.g., from the storage asset to the transfer asset); (4) one or more visual indications of a risk level associated with the transfer of personal data; and/or (5) any other suitable information related to the one or more data assets, the transfer of data between/among the one or more data assets, access to data stored or collected
  • the visual indication of a particular asset may comprise a box, symbol, shape, or other suitable visual indicator.
  • the visual indication may comprise one or more labels (e.g., a name of each particular data asset, a type of the asset, etc.).
  • the visual indication of a flow of data may comprise one or more arrows.
  • the visual representation of the data model may comprise a data flow, flowchart, or other suitable visual representation.
  • the system is configured to display (e.g., to a user) the generated visual representation of the data model on a suitable display device.
  • Figures 4-10 depict exemplary data models according to various embodiments of the system described herein.
  • Figure 4 depicts an exemplary data model that does not include a particular processing activity (e.g., that is not associated with a particular processing activity).
  • a particular data asset e.g., a primary data asset
  • a particular company e.g., organization
  • organization e.g., organization
  • sub-organization of a particular organization e.g., organization
  • the particular asset may be associated with one or more collection assets (e.g., one or more data subjects from whom personal data is collected for storage by the particular asset), one or more parties that have access to data stored by the particular asset, one or more transfer assets (e.g., one or more assets to which data stored by the particular asset may be transferred), etc.
  • collection assets e.g., one or more data subjects from whom personal data is collected for storage by the particular asset
  • transfer assets e.g., one or more assets to which data stored by the particular asset may be transferred
  • a particular data model for a particular asset may include a plurality of data elements.
  • a system may be configured to substantially automatically identify one or more types of data elements for inclusion in the data model, and automatically generate a data model that includes those identified data elements (e.g., even if one or more of those data elements must remain unpopulated because the system may not initially have access to a value for the particular data element).
  • the system may be configured to store a placeholder for a particular data element until the system is able to populate the particular data element with accurate data.
  • the data model shown in Figure 4 may represent a portion of an overall data model.
  • the transfer asset depicted may serve as a storage asset for another portion of the data model.
  • the transfer asset may be associated with a respective one or more of the types of data elements described above.
  • the system may generate a data model that may build upon itself to comprise a plurality of layers as the system adds one or more new data assets, attributes, etc.
  • a particular data model may indicate one or more parties that have access to and/or use of the primary asset (e.g., storage asset).
  • the system may be configured to enable the one or more parties to access one or more pieces of data (e.g., personal data) stored by the storage asset.
  • the data model may further comprise one or more collection assets (e.g., one or more data assets or individuals from which the storage asset receives data such as personal data).
  • the collection assets comprise a data subject (e.g., an individual that may provide data to the system for storage in the storage asset) and a collection asset (e.g., which may transfer one or more pieces of data that the collection asset has collected to the storage asset).
  • Figure 5 depicts a portion of an exemplary data model that is populated for the primary data asset Gusto.
  • Gusto is a software application that, in the example shown in Figure 5, may serve as a human resources service that contains financial, expense, review, time and attendance, background, and salary information for one or more employees of a particular organization (e.g., GeneriTech).
  • the primary asset e.g., Gusto
  • the HR e.g., Human Resources
  • the primary asset, Gusto may collect financial information from one or more data subjects (e.g., employees of the particular organization), receive expense information transferred from Expensify (e.g., expensing software), and receive time and attendance data transferred from Kronos (e.g., timekeeping software).
  • access to the information collected and/or stored by Gusto may include, for example: (1) an ability to view and administer salary and background information by HR employees, and (2) an ability to view and administer employee review information by one or more service managers.
  • personal and other data collected and stored by Gusto e.g., salary information, etc.
  • the system may be configured to generate a data model based around Gusto that illustrates a flow of personal data utilized by Gusto.
  • the data model in this example illustrates, for example, a source of personal data collected, stored and/or processed by Gusto, a destination of such data, an indication of who has access to such data within Gusto, and an organization and department responsible for the information collected by Gusto.
  • the data model and accompanying visual representation e.g., data map
  • the system may be utilized in the context of compliance with one or more record keeping requirements related to the collection, storage, and processing of personal data.
  • Figures 6 and 7 depict an exemplary data model and related example that is similar, in some respects, to the data model and example of Figures 4 and 5.
  • the exemplary data model and related example include a specific business process and processing activity that is associated with the primary asset (Gusto).
  • the business process is compensation and the specific processing activity is direct deposit generation in Gusto.
  • the collection and transfer of data related to the storage asset of Gusto is based on a need to generate direct deposits through Gusto in order to compensate employees.
  • Gusto generates the information needed to conduct a direct deposit (e.g., financial and salary information) and then transmits this information to: (1) a company bank system for execution of the direct deposit; (2) Quickbooks for use in documenting the direct deposit payment; and (3) HR File cabinet for use in documenting the salary info and other financial information.
  • a direct deposit e.g., financial and salary information
  • the system when generating such a data model, particular pieces of data (e.g., data attributes, data elements) may not be readily available to the system.
  • the system is configured to identify a particular type of data, create a placeholder for such data in memory, and seek out (e.g., scan for and populate) an appropriate piece of data to further populate the data model.
  • the system may identify Gusto as a primary asset and recognize that Gusto stores expense information.
  • the system may then be configured to identify a source of the expense information (e.g., Expensify).
  • Figure 8 depicts an exemplary screen display 800 that illustrates a visual representation (e.g., visual data map) of a data model (e.g., a data inventory).
  • the data map provides a visual indication of a flow of data collected from particular data subjects (e.g., employees 801).
  • the data map illustrates that three separate data assets receive data (e.g., which may include personal data) directly from the employees 801.
  • these three data assets include Kronos 803 (e.g., a human resources software application), Workday 805 (e.g., a human resources software application), and ADP 807 (e.g., a human resources software application and payment processor).
  • Kronos 803 e.g., a human resources software application
  • Workday 805 e.g., a human resources software application
  • ADP 807 e.g., a human resources software application and payment processor
  • the data map indicates a transfer of data from Workday 805 to ADP 807 as well as to a Recovery Datacenter 809 and a London HR File Center 811.
  • the Recovery Datacenter 809 and London HR File Center 811 may comprise additional data assets in the context of the data model illustrated by the data map shown in Figure 8.
  • the Recover Datacenter 809 may include, for example, one or more computer servers (e.g., backup servers).
  • the London HR File Center 811 may include, for example, one or more databases (e.g., such as the One or More Databases 140 shown in Figure 1). AS shown in Figure 8, each particular data asset depicted in the data map may be shown along with a visual indication of the type of data asset.
  • Kronos 803, Workday 805, and ADP 807 are depicted adjacent a first icon type (e.g., a computer monitor), while Recover Datacenter 809 and London HR File Center 811 are depicted adjacent a second and third icon type respectively (e.g., a server cluster and a file folder).
  • first icon type e.g., a computer monitor
  • second and third icon type respectively
  • the system may be configured to visually indicate, via the data model, particular information related to the data model in a relatively minimal manner.
  • Figure 9 depicts an exemplary screen display 900 that illustrates a data map of a plurality of assets 905 in tabular form (e.g., table form).
  • a table that includes one or more inventory attributes of each particular asset 905 in the table may indicate, for example: (1) a managing organization 910 of each respective asset 905; (2) a hosting location 915 of each respective asset 905 (e.g., a physical storage location of each asset 905); (3) a type 920 of each respective asset 905, if known (e.g., a database, software application, server, etc.); (4) a processing activity 925 associated with each respective asset 905; and/or (5) a status 930 of each particular data asset 905.
  • the status 930 of each particular asset 905 may indicate a status of the asset 905 in the discovery process. This may include, for example: (1) a "new" status for a particular asset that has recently been discovered as an asset that processes, stores, or collects personal data on behalf of an organization (e.g., discovered via one or more suitable techniques described herein); (2) an "in discovery" status for a particular asset for which the system is populating or seeking to populate one or more inventory attributes, etc.
  • Figure 10 depicts an exemplary data map 1000 that includes an asset map of a plurality of data assets 1005A-F, which may, for example, be utilized by a particular entity in the collection, storage, and/or processing of personal data.
  • the plurality of data assets 1005A-F may have been discovered using any suitable technique described herein (e.g., one or more intelligent identity scanning techniques, one or more questionnaires, one or more application programming interfaces, etc.).
  • a data inventory for each of the plurality of data assets 1005A-F may define, for each of the plurality of data assets 1005A-F a respective inventory attribute related to a storage location of the data asset.
  • the system may be configured to generate a map that indicates a location of the plurality of data assets 1005A-F for a particular entity.
  • locations that contain a data asset are indicated by circular indicia that contain the number of assets present at that location.
  • the locations are broken down by country.
  • the asset map may distinguish between internal assets (e.g., first party servers, etc.) and external/third party assets (e.g., third party owned servers or software applications that the entity utilizes for data storage, transfer, etc.).
  • the system is configured to indicate, via the visual representation, whether one or more assets have an unknown location (e.g., because the data model described above may be incomplete with regard to the location).
  • the system may be configured to: (1) identify the asset with the unknown location; (2) use one or more data modeling techniques described herein to determine the location (e.g., such as pinging the asset, generating one or more questionnaires for completion by a suitable individual, etc.); and (3) update a data model associated with the asset to include the location.
  • a Data Model Population Module 1100 is configured to: (1) determine one or more unpopulated inventory attributes in a data model; (2) determine one or more attribute values for the one or more unpopulated inventory attributes; and (3) modify the data model to include the one or more attribute values.
  • the system begins, at Step 1110, by analyzing one or more data inventories for each of the one or more data assets in the data model.
  • the system may, for example, identify one or more particular data elements (e.g., inventory attributes) that make up the one or more data inventories.
  • the system may, in various embodiments, scan one or more data structures associated with the data model to identify the one or more data inventories.
  • the system is configured to build an inventory of existing (e.g., known) data assets and identify inventory attributes for each of the known data assets.
  • the system is configured to determine, for each of the one or more data inventories, one or more populated inventory attributes and one or more unpopulated inventory attributes (e.g., and/or one or more unpopulated data assets within the data model).
  • the system may determine that, for a particular asset, there is a destination asset.
  • the destination asset may be known (e.g., and already stored by the system as part of the data model).
  • the destination asset may be unknown (e.g., a data element that comprises the destination asset may comprise a placeholder or other indication in memory for the system to populate the unpopulated inventory attribute (e.g., data element).
  • a particular storage asset may be associated with a plurality of inventory assets (e.g., stored in a data inventory associated with the storage asset).
  • the plurality of inventory assets may include an unpopulated inventory attribute related to a type of personal data stored in the storage asset.
  • the system may, for example, determine that the type of personal data is an unpopulated inventory asset for the particular storage asset.
  • the system is configured to determine, for each of the one or more unpopulated inventory attributes, one or more attribute values.
  • the system may determine the one or more attribute values using any suitable technique (e.g., any suitable technique for populating the data model).
  • the one or more techniques for populating the data model may include, for example: (1) obtaining data for the data model by using one or more questionnaires associated with a particular privacy campaign, processing activity, etc.; (2) using one or more intelligent identity scanning techniques discussed herein to identify personal data stored by the system and then map such data to a suitable data model; (3) using one or more application programming interfaces (API) to obtain data for the data model from another software application; and/or (4) using any other suitable technique. Exemplary techniques for determining the one or more attribute values are described more fully below.
  • the system may be configured to use such techniques or other suitable techniques to populate one or more unpopulated data assets within the data model.
  • the system modifies the data model to include the one or more attribute values for each of the one or more unpopulated inventory attributes.
  • the system may, for example, store the one or more attributes values in computer memory, associate the one or more attribute values with the one or more unpopulated inventory attributes, etc.
  • the system may modify the data model to include the one or more data assets identified as filling one or more vacancies left within the data model by the unpopulated one or more data assets.
  • the system is configured to store the modified data model in memory.
  • the system is configured to store the modified data model in the One or More Databases 140, or in any other suitable location.
  • the system is configured to store the data model for later use by the system in the processing of one or more data subject access requests.
  • the system is configured to store the data model for use in one or more privacy impact assessments performed by the system.
  • a Data Population Questionnaire Generation Module 1200 is configured to generate a questionnaire (e.g., one or more questionnaires) comprising one or more questions associated with one or more particular unpopulated data attributes, and populate the unpopulated data attributes based at least in part on one or more responses to the questionnaire.
  • a questionnaire e.g., one or more questionnaires
  • the system may be configured to populate the unpopulated data attributes based on one or more responses to existing questionnaires.
  • the one or more questionnaires may comprise one or more processing activity questionnaires (e.g., privacy impact assessments, data privacy impact assessments, etc.) configured to elicit one or more pieces of data related to one or more undertakings by an organization related to the collection, storage, and/or processing of personal data (e.g., processing activities).
  • the system is configured to generate the questionnaire (e.g., a questionnaire template) based at least in part on one or more processing activity attributes, data asset attributes (e.g., inventory attributes), or other suitable attributes discussed herein.
  • the system when executing the Data Population Questionnaire Generation Module 1200, the system begins, at Step 1210, by identifying one or more unpopulated data attributes from a data model.
  • the system may, for example, identify the one or more unpopulated data attributes using any suitable technique described above.
  • the one or more unpopulated data attributes may relate to, for example, one or more processing activity or asset attributes such as: (1) one or more processing activities associated with a particular data asset; (2) transfer data associated with the particular data asset (e.g., how and where the data stored and/or collected by the particular data asset is being transferred to and/or from); (3) personal data associated with the particular data assets asset (e.g., what type of personal data is collected and/or stored by the particular data asset; how, and from where, the data is collected, etc.); (4) storage data associated with the personal data (e.g., whether the data is being stored, protected and deleted); and (5) any other suitable attribute related to the collection, use, and transfer of personal data by one or more data assets or via one or more processing activities.
  • processing activity or asset attributes such as: (1) one or more processing activities associated with a particular data asset; (2) transfer data associated with the particular data asset (e.g., how and where the data stored and/or collected by the particular data asset is being transferred to and/or from); (3) personal data associated with the
  • the one or more unpopulated inventory attributes may comprise one or more other pieces of information such as, for example: (1) the type of data being stored by the particular data asset; (2) an amount of data stored by the particular data asset; (3) whether the data is encrypted by the particular data asset; (4) a location of the stored data (e.g., a physical location of one or more computer servers on which the data is stored by the particular data asset); etc.
  • the system generates a questionnaire (e.g., a questionnaire template) comprising one or more questions associated with one or more particular unpopulated data attributes.
  • the one or more particulate unpopulated data attributes may relate to, for example, a particular processing activity or a particular data asset (e.g., a particular data asset utilized as part of a particular processing activity).
  • the one or more questionnaires comprise one or more questions associated with the unpopulated data attribute. For example, if the data model includes an unpopulated data attribute related to a location of a server on which a particular asset stores personal data, the system may generate a questionnaire associated with a processing activity that utilizes the asset (e.g., or a questionnaire associated with the asset). The system may generate the questionnaire to include one or more questions regarding the location of the server.
  • the system maps one or more responses to the one or more questions to the associated one or more particular unpopulated data attributes.
  • the system may, for example, when generating the questionnaire, associate a particular question with a particular unpopulated data attribute in computer memory.
  • the questionnaire may comprise a plurality of question/answer pairings, where the answer in the question/answer pairings maps to a particular inventory attribute for a particular data asset or processing activity.
  • the system may, upon receiving a response to the particular question, substantially automatically populate the particular unpopulated data attribute.
  • the system modifies the data model to populate the one or more responses as one or more data elements for the one or more particular unpopulated data attributes.
  • the system is configured to modify the data model such that the one or more responses are stored in association with the particular data element (e.g., unpopulated data attribute) to which the system mapped it at Step 1230.
  • the system is configured to store the modified data model in the One or More Databases 140, or in any other suitable location.
  • the system is configured to store the data model for later use by the system in the processing of one or more data subject access requests.
  • the system is configured to store the data model for use in one or more privacy impact assessments performed by the system.
  • the system may be configured to modify the questionnaire based at least in part on the one or more responses.
  • the system may, for example, substantially dynamically add and/or remove one or more questions to/from the questionnaire based at least in part on the one or more responses (e.g., one or more response received by a user completing the questionnaire).
  • the system may, in response to the user providing a particular inventory attribute or new asset, generates additional questions that relate to that particular inventory attribute or asset.
  • the system may, as the system adds additional questions, substantially automatically map one or more responses to one or more other inventory attributes or assets.
  • the system may substantially automatically generate one or more additional questions related to, for example, an encryption level of the storage, who has access to the storage location, etc.
  • the system may modify the data model to include one or more additional assets, data attributes, inventory attributes, etc. in response to one or more questionnaire responses.
  • the system may modify a data inventory for a particular asset to include a storage encryption data element (which specifies whether the particular asset stores particular data in an encrypted format) in response to receiving such data from a questionnaire. Modification of a questionnaire is discussed more fully below with respect to Figure 13.
  • Figure 13 depicts an exemplary process flow 1300 for populating a data model (e.g., modifying a data model to include a newly discovered data asset, populating one or more inventory attributes for a particular processing activity or data asset, etc.).
  • Figure 13 depicts one or more exemplary data relationships between one or more particular data attributes (e.g., processing activity attributes and/or asset attributes), a questionnaire template (e.g., a processing activity template and/or a data asset template), a completed questionnaire (e.g., a processing activity assessment and/or a data asset assessment), and a data inventory (e.g., a processing activity inventory and/or an asset inventory).
  • the system is configured to: (1) identify new data assets; (2) generate an asset inventory for identified new data assets; and (3) populate the generated asset inventories. Systems and methods for populating the generated inventories are described more fully below.
  • a system may be configured to map particular processing activity attributes 1320A to each of: (1) a processing activity template 1330A; and (2) a processing activity data inventory 1310A.
  • the processing activity template 1330A may comprise a plurality of questions (e.g., as part of a questionnaire), which may, for example, be configured to elicit discovery of one or more new data assets.
  • the plurality of questions may each correspond to one or more fields in the processing activity inventory 1310A, which may, for example, define one or more inventory attributes of the processing activity.
  • a system may be configured to map particular asset attributes 1320B to each of: (1) an asset template 1330BA; and (2) an asset inventory 1310A.
  • the asset template 1330B may comprise a plurality of questions (e.g., as part of a questionnaire), which may, for example, be configured to elicit discovery of one or more processing activities associated with the asset and/or one or more inventory attributes of the asset.
  • the plurality of questions may each correspond to one or more fields in the asset inventory 1310B, which may, for example, define one or more inventory attributes of the asset.
  • the system is configured to provide an asset assessment 1340B to one or more individuals for completion.
  • the system is configured to launch the asset assessment 1340B from the asset inventory 1310B and further configured to create the asset assessment 1340B from the asset template 1330B.
  • the asset assessment 1340B may comprise, for example, one or more questions related to the data asset.
  • the system may, in various embodiments, be configured to map one or more responses provided in the asset assessment 1340B to one or more corresponding fields in the asset inventory 1310B.
  • the system may then be configured to modify the asset inventory 1310B (e.g., and/or a related processing activity inventory 1310A) to include the one or more responses, and store the modified inventory in computer memory.
  • the system may be configured to approve an asset assessment 1340B (e.g., receive approval of the assessment) prior to feeding the asset inventory attribute values into one or more fields and/or cells of the inventory.
  • Figure 13 further includes a detail view 1350 of a relationship between particular data attributes 1320C with an exemplary data inventory 13 IOC and a questionnaire template 1330C.
  • a particular attribute name may map to a particular question title in a template 1330C as well as to a field name in an exemplary data inventory 13 IOC.
  • the system may be configured to populate (e.g., automatically populate) a field name for a particular inventory 13 IOC in response to a user providing a question title as part of a questionnaire template 1330C.
  • a particular attribute description may map to a particular question description in a template 1330C as well as to a tooltip on a fieldname in an exemplary data inventory 13 IOC.
  • a particular data attribute may require a response type of, for example: (1) a name of an organization responsible for a data asset (e.g., a free form response); (2) a number of days that data is stored by the data asset (e.g., an integer value); and/or (3) any other suitable response type.
  • a response type of, for example: (1) a name of an organization responsible for a data asset (e.g., a free form response); (2) a number of days that data is stored by the data asset (e.g., an integer value); and/or (3) any other suitable response type.
  • the system may be configured to map a one or more attribute values to one or more answer choices in a template 1330C as well as to one or more lists and/or responses in a data inventory 13 IOC. The system may then be configured to populate a field in the data inventory 13 IOC with the one or more answer choices provided in a response to a question template 1330C with one or more attribute values.
  • a template for an asset may include, for example: (1) one or more questions requesting general information about the asset; (2) one or more security-related questions about the asset; (3) one or more questions regarding how the data asset disposes of data that it uses; and/or (4) one or more questions regarding processing activities that involve the data asset.
  • each of these one or more sections may comprise one or more specific questions that may map to particular portions of a data model (e.g., a data map).
  • the system is configured to enable a user to modify a default template (e.g., or a system-created template) by, for example, adding additional sections, adding one or more additional questions to a particular section, etc.
  • the system may provide one or more tools for modifying the template.
  • the system may provide a user with a draft and drop question template 1510, from which the user may select a question type (e.g., textbox, multiple choice, asset attributes, data subjects, etc.).
  • the system may be further configured to enable a user to publish a completed template (e.g., for use in a particular assessment).
  • the system may be configured to substantially automatically publish the template.
  • the system may create the assessment based at least in part on a template associated with the asset, and transmit the assessment to a suitable individual for completion (e.g., and/or transmit a request to the individual to complete the assessment).
  • Figure 18 depicts an exemplary assessment 1800 which a user may encounter in response to receiving a request to complete the assessment as described above with respect to Figures 16 and 17.
  • the assessment 1800 may include one or more questions that map to the one or more unpopulated attributes for the asset shown in Figure 16.
  • the one or more questions may include a question related to a description of the asset, which may include a free form text box 1820 for providing a description of the asset.
  • Figure 19 depicts an exemplary screen display 1900 with the text box 1920 completed, where the description includes a value of "Value_l".
  • the user may have renamed "New Asset” (e.g., which may have included a default or placeholder name) shown in Figures 16 and 17 to "7 th Asset.”
  • the exemplary screen display 2000 depicts the listing of assets 2010 from Figure 16 with some additional attributes populated.
  • the Description 2025 e.g., "Value_l”
  • the system may be configured to map the provided description to the attribute value associated with the description of the asset in the data inventory.
  • the system may have then modified the data inventory for the asset to include the description attribute.
  • the system is configured to store the modified data inventory as part of a data model (e.g., in computer memory).
  • the system may be configured to modify a questionnaire in response to (e.g., based on) one or more responses provided by a user completing the questionnaire.
  • the system is configured to modify the questionnaire substantially on-the-fly (e.g., as the user provides each particular answer).
  • Figure 22 depicts an interface 2200 that includes a second question 2220 that differs from the second question 2120 shown in Figure 21.
  • the system may substantially automatically modify the second question 2120 from Figure 21 to the second question 2220 from Figure 22 (e.g., such that the second question 2220 includes one or more follow up questions or requests for additional information based on the response to the first question 2110 in Figure 21).
  • the second question 2220 requests a description of the activity that is being pursued.
  • the system may not modify the questionnaire to include the second question 2220 from Figure 22, because the system may already store information related to a description of the processing activity at issue.
  • any suitable question described herein may include a tooltip 2225 on a field name (e.g., which may provide one or more additional pieces of information to guide a user's response to the questionnaire and/or assessment).
  • Figures 23 and 24 depict additional exemplary assessment questions.
  • the questions shown in these figures relate to, for example, particular data elements processed by various aspects of a processing activity.
  • FIG. 25 depicts a dashboard 2500 that includes an accounting of one or more assessments that have been completed, are in progress, or require completion by a particular organization.
  • the dashboard 2500 shown in this figure is configured to provide information relate to the status of one or more outstanding assessments.
  • the dashboard may indicate that, based on a fact that a number of assessments are still in progress or incomplete, that a particular data model for an entity, data asset, processing activity, etc. remains incomplete.
  • an incomplete nature of a data model may raise one or more flags or indicate a risk that an entity may not be in compliance with one or more legal or industry requirements related to the collection, storage, and/or processing of personal data.
  • the Intelligent Identity Scanning Module 2600 is configured to scan one or more data sources to identify personal data stored on one or more network devices for a particular organization, analyze the identified personal data, and classify the personal data (e.g., in a data model) based at least in part on a confidence score derived using one or more machine learning techniques.
  • the confidence score may be and/or comprise, for example, an indication of the probability that the personal data is actually associated with a particular data subject (e.g., that there is at least an 80% confidence level that a particular phone number is associated with a particular individual.)
  • the system When executing the Intelligent Identity Scanning Module 2600, the system begins, at Step 2610, by connecting to one or more databases or other data structures, and scanning the one or more databases to generate a catalog of one or more individuals and one or more pieces of personal information associated with the one or more individuals.
  • the system may, for example, be configured to connect to one or more databases associated with a particular organization (e.g., one or more databases that may serve as a storage location for any personal or other data collected, processed, etc. by the particular organization, for example, as part of a suitable processing activity.
  • a particular organization may use a plurality of one or more databases (e.g., the One or More Databases 140 shown in Figure 1), a plurality of servers (e.g., the One or More Third Party Servers 160 shown in Figure 1), or any other suitable data storage location in order to store personal data and other data collected as part of any suitable privacy campaign, privacy impact assessment, processing activity, etc.
  • databases e.g., the One or More Databases 140 shown in Figure 1
  • servers e.g., the One or More Third Party Servers 160 shown in Figure 1
  • any other suitable data storage location e.g., the One or More Third Party Servers 160 shown in Figure 1
  • the system is configured to scan the one or more databases by searching for particular data fields comprising one or more pieces of information that may include personal data.
  • the system may, for example, be configured to scan and identify one of more pieces of personal data such as: (1) name; (2) address; (3) telephone number; (4) e-mail address; (5) social security number; (6) information associated with one or more credit accounts (e.g., credit card numbers); (7) banking information; (8) location data; (9) internet search history; (10) non-credit account data; and/or (11) any other suitable personal information discussed herein.
  • the system is configured to scan for a particular type of personal data (e.g., or one or more particular types of personal data).
  • the system may, in various embodiments, be further configured to generate a catalog of one or more individuals that also includes one or more pieces of personal information (e.g., personal data) identified for the individuals during the scan.
  • the system may, for example, in response to discovering one or more pieces of personal data in a particular storage location, identify one or more associations between the discovered pieces of personal data.
  • a particular database may store a plurality of individuals' names in association with their respective telephone numbers.
  • One or more other databases may include any other suitable information.
  • the system may, for example, generate the catalog to include any information associated with the one or more individuals identified in the scan.
  • the system may, for example, maintain the catalog in any suitable format (e.g., a data table, etc.).
  • the system in addition to connecting to a database, may be configured to: (1) access an application through one or more application programming interfaces (APIs); (2) use one or more screen scraping techniques on an end user page to identify and analyze each field on the page; and/or (3) connect to any other suitable data structure in order to generate the catalog of individuals and personal information associated with each of the individuals.
  • the system may be configured to analyze one or more access logs and applications set up through a system active directory or SSO portal for which one or more applications might contain certain data for user groups. The system may then be configured to analyze an email environment to identify one or more links to particular business applications, which may, for example, be in use by an entity and contain certain data.
  • the system may be configured to analyze one or more system log files (Syslog) from a security environment to capture which particular applications an entity may be using in order to discover such applications.
  • Syslog system log files
  • the system is configured to scan one or more structured and/or unstructured data repositories based at least in part on the generated catalog to identify one or more attributes of data associated with the one or more individuals.
  • the system may, for example, be configured to utilize information discovered during the initial scan at Step 2610 to identify the one or more attributes of data associated with the one or more individuals.
  • the catalog generated at Step 2610 may include a name, address, and phone number for a particular individual.
  • the system may be configured, at Step 2620, to scan the one or more structured and/or unstructured data repositories to identify one or more attributes that are associated with one or more of the particular individual's name, address and/or phone number.
  • a particular data repository may store banking information (e.g., a bank account number and routing number for the bank) in association with the particular individual's address.
  • the system may be configured to identify the banking information as an attribute of data associated with the particular individual.
  • the system may be configured to identify particular data attributes (e.g., one or more pieces of personal data) stored for a particular individual by identifying the particular data attributes using information other than the individual's name.
  • the system is configured to analyze and correlate the one or more attributes and metadata for the scanned one or more structured and/or unstructured data repositories.
  • the system is configured to correlate the one or more attributes with metadata for the associated data repositories from which the system identified the one or more attributes. In this way, the system may be configured to store data regarding particular data repositories that store particular data attributes.
  • the system may be configured to cross-reference the data repositories that are discovered to store one or more attributes of personal data associated with the one or more individuals with a database of known data assets.
  • the system is configured to analyze the data repositories to determine whether each data repository is part of an existing data model of data assets that collect, store, and/or process personal data.
  • the system may be configured to identify the data repository as a new data asset (e.g., via asset discovery), and take one or more actions (e.g., such as any suitable actions described herein) to generate and populate a data model of the newly discovered data asset.
  • This may include, for example: (1) generating a data inventory for the new data asset; (2) populating the data inventory with any known attributes associated with the new data asset; (3) identifying one or more unpopulated (e.g., unknown) attributes of the data asset; and (4) taking any suitable action described herein to populate the unpopulated data attributes.
  • the system my, for example: (1) identify a source of the personal data stored in the data repository that led to the new asset discovery; (2) identify one or more relationships between the newly discovered asset and one or more known assets; and/or (3) etc.
  • the system is configured to use one or more machine learning techniques to categorize one or more data elements from the generated catalog, analyze a flow of the data among the one or more data repositories, and/or classify the one or more data elements based on a confidence score as discussed below.
  • the system in various embodiments, is configured to receive input from a user confirming or denying a categorization of the one or more data elements, and, in response, modify the confidence score.
  • the system is configured to iteratively repeat Steps 2640 and 2650. In this way, the system is configured to modify the confidence score in response to a user confirming or denying the accuracy of a categorization of the one or more data elements.
  • the system is configured to prompt a user (e.g., a system administrator, privacy officer, etc.) to confirm that a particular data element is, in fact, associated with a particular individual from the catalog.
  • the system may, in various embodiments, be configured to prompt a user to confirm that a data element or attribute discovered during one or more of the scans above were properly categorized at Step 2640.
  • the system is configured to modify the confidence score based at least in part on receiving one or more confirmations that one or more particular data elements or attributes discovered in a particular location during a scan are associated with particular individuals from the catalog.
  • the system may be configured to increase the confidence score in response to receiving confirmation that particular types of data elements or attributes discovered in a particular storage location are typically confirmed as being associated with particular individuals based on one or more attributes for which the system was scanning.
  • FIG. 27 depicts an exemplary technical platform via which the system may perform one or more of the steps described above with respect to the Intelligent Identity Scanning Module 2600.
  • an Intelligent Identity Scanning System 2600 comprises an Intelligent Identity Scanning Server 130, such as the Intelligent Identity Scanning Server 130 described above with respect to Figure 1.
  • the Intelligent Identity Scanning Server 130 may, for example, comprise a processing engine (e.g., one or more computer processors).
  • the Intelligent Identity Scanning Server 130 may include any suitable cloud hosted processing engine (e.g., one or more cloud-based computer servers).
  • the Intelligent Identity Scanning Server 130 is hosted in a Microsoft Azure cloud.
  • the Intelligent Identity Scanning Server 130 is configured to sit outside one or more firewalls (e.g., such as the firewall 195 shown in Figure 26). In such embodiments, the Intelligent Identity Scanning Server 130 is configured to access One or More Remote Computing Devices 150 through the Firewall 195 (e.g., one or more firewalls) via One or More Networks 115 (e.g., such as any of the One or More Networks 115 described above with respect to Figure 1).
  • One or More Networks 115 e.g., such as any of the One or More Networks 115 described above with respect to Figure 1).
  • the one or more computer networks associated with the particular organization comprise One or More Privileged Networks 165.
  • the one or more computer networks comprise one or more network segments connected via one or more suitable routers, one or more suitable network hubs, one or more suitable network switches, etc.
  • various components that make up one or more parts of the one or more computer networks associated with the particular organization may store personal data (e.g., such as personal data stored on the One or More Third Party Servers 160, the One or More Databases 140, etc.).
  • the system is configured to perform one or more steps related to the Intelligent Identity Scanning Server 2600 in order to identify the personal data for the purpose of generating the catalog of individuals described above (e.g., and/or identify one or more data assets within the organization's network that store personal data)
  • the One or More Remote Computing Devices 150 may store a software application (e.g., the Intelligent Identity Scanning Module).
  • the system may be configured to provide the software application for installation on the One or More Remote Computing Devices 150.
  • the software application may comprise one or more virtual machines.
  • the one or more virtual machines may be configured to perform one or more of the steps described above with respect to the Intelligent Identity Scanning Module 2600 (e.g., perform the one or more steps locally on the One or More Remote Computing Devices 150).
  • These one or more suitable purposes may include, for example, running any of the one or more modules described herein, storing hashed and/or non-hashed information (e.g., personal data, personally identifiable data, catalog of individuals, etc.), storing and running one or more searching and/or scanning engines (e.g., Elasticsearch), etc..
  • hashed and/or non-hashed information e.g., personal data, personally identifiable data, catalog of individuals, etc.
  • searching and/or scanning engines e.g., Elasticsearch
  • the Intelligent Identity Scanning System 2700 is configured to reduce an impact on a performance of the One or More Remote Computing Devices 150, One or More Third Party Servers 160 and other components that make up one or more segments of the one or more computer networks associated with the particular organization.
  • the Intelligent Identity Scanning System 2700 may be configured to utilize one or more suitable bandwidth throttling techniques.
  • the Intelligent Identity Scanning System 2700 is configured to limit scanning (e.g., any of the one or more scanning steps described above with respect to the Intelligent Identity Scanning Module 2600) and other processing steps (e.g., one or more steps that utilize one or more processing resources) to non-peak times (e.g., during the evening, overnight, on weekends and/or holidays, etc.).
  • the system is configured to limit performance of such processing steps to backup applications and data storage locations.
  • the system may, for example, use one or more sampling techniques to decrease a number of records required to scan during the personal data discovery process.
  • Figure 28 depicts an exemplary asset access methodology that the system may utilize in order to access one or more network devices that may store personal data (e.g., or other personally identifiable information).
  • the system may be configured to access the one or more network devices using a locally deployed software application (e.g., such as the software application described immediately above).
  • the software application is configured to route identity scanning traffic through one or more gateways, configure one or more ports to accept one or more identity scanning connections, etc.
  • the system may be configured to utilize one or more credential management techniques to access one or more privileged network portions.
  • the system may, in response to identifying particular assets or personally identifiable information via a scan, be configured to retrieve schema details such as, for example, an asset ID, Schema ID, connection string, credential reference URL, etc.
  • schema details such as, for example, an asset ID, Schema ID, connection string, credential reference URL, etc.
  • the system may be configured to identify and store a location of any discovered assets or personal data during a scan.
  • Request Fulfillment Module 2900 is configured to receive a data subject access request, process the request, and fulfill the request based at least in part on one or more request parameters.
  • an organization, corporation, etc. may be required to provide information requested by an individual for whom the organization stores personal data within a certain time period (e.g., 30 days).
  • a certain time period e.g. 30 days.
  • an organization may be required to provide an individual with a listing of, for example: (1) any personal data that the organization is processing for an individual, (2) an explanation of the categories of data being processed and the purpose of such processing; and/or (3) categories of third parties to whom the data may be disclosed.
  • Various privacy and security policies may provide data subjects (e.g., individuals, organizations, or other entities) with certain rights related to the data subject's personal data that is collected, stored, or otherwise processed by an organization.
  • data subjects e.g., individuals, organizations, or other entities
  • certain rights related to the data subject's personal data that is collected, stored, or otherwise processed by an organization.
  • These rights may include, for example: (1) a right to obtain confirmation of whether a particular organization is processing their personal data; (2) a right to obtain information about the purpose of the processing (e.g., one or more reasons for which the personal data was collected); (3) a right to obtain information about one or more categories of data being processed (e.g., what type of personal data is being collected, stored, etc.); (4) a right to obtain information about one or more categories of recipients with whom their personal data may be shared (e.g., both internally within the organization or externally); (5) a right to obtain information about a time period for which their personal data will be stored (e.g., or one or more criteria used to determine that time period); (6) a right to obtain a copy of any personal data being processed (e.g., a right to receive a copy of their personal data in a commonly used, machine-readable format); (7) a right to request erasure (e.g., the right to be forgotten), rectification (e.g., correction or deletion of inaccurate data),
  • a particular organization may undertake a plurality of different privacy campaigns, processing activities, etc. that involve the collection and storage of personal data.
  • each of the plurality of different processing activities may collect redundant data (e.g., may collect the same personal data for a particular individual more than once), and may store data and/or redundant data in one or more particular locations (e.g., on one or more different servers, in one or more different databases, etc.).
  • a particular organization may store personal data in a plurality of different locations which may include one or more known and/or unknown locations.
  • a data subject access request fulfillment system may utilize one or more data model generation and population techniques (e.g., such as any suitable technique described herein) to create a centralized data map with which the system can identify personal data stored, collected, or processed for a particular data subject, a reason for the processing, and any other information related to the processing.
  • data model generation and population techniques e.g., such as any suitable technique described herein
  • the system begins, at Step 2110, by receiving a data subject access request.
  • the system receives the request via a suitable web form.
  • the request comprises a particular request to perform one or more actions with any personal data stored by a particular organization regarding the requestor.
  • the request may include a request to view one or more pieces of personal data stored by the system regarding the requestor.
  • the request may include a request to delete one or more pieces of personal data stored by the system regarding the requestor.
  • the request may include a request to update one or more pieces of personal data stored by the system regarding the requestor.
  • the request may include a request based on any suitable right afforded to a data subject, such as those discussed above.
  • the system is configured to process the request by identifying and retrieving one or more pieces of personal data associated with the requestor that are being processed by the system.
  • the system is configured to identify any personal data stored in any database, server, or other data repository associated with a particular organization.
  • the system is configured to use one or more data models, such as those described above, to identify this personal data and suitable related information (e.g., where the personal data is stored, who has access to the personal data, etc.).
  • the system is configured to use intelligent identity scanning (e.g., as described above) to identify the requestor's personal data and related information that is to be used to fulfill the request.
  • the system is configured to use one or more machine learning techniques to identify such personal data.
  • the system may identify particular stored personal data based on, for example, a country in which a website that the data subject request was submitted is based, or any other suitable information.
  • the system is configured to scan and/or search one or more existing data models (e.g., one or more current data models) in response to receiving the request in order to identify the one or more pieces of personal data associated with the requestor.
  • the system may, for example, identify, based on one or more data inventories (e.g., one or more inventory attributes) a plurality of storage locations that store personal data associated with the requestor.
  • the system may be configured to generate a data model or perform one or more scanning techniques in response to receiving the request (e.g., in order to automatically fulfill the request).
  • the system is configured to take one or more actions based at least in part on the request.
  • the system is configured to take one or more actions for which the request was submitted (e.g., display the personal data, delete the personal data, correct the personal data, etc.).
  • the system is configured to take the one or more actions substantially automatically.
  • the system in response a data subject submitting a request to delete their personal data from an organization's systems, may: (1) automatically determine where the data subject's personal data is stored; and (2) in response to determining the location of the data (which may be on multiple computing systems), automatically facilitate the deletion of the data subject's personal data from the various systems (e.g., by automatically assigning a plurality of tasks to delete data across multiple business systems to effectively delete the data subject's personal data from the systems).
  • the step of facilitating the deletion may comprise, for example: (1) overwriting the data in memory; (2) marking the data for overwrite; (2) marking the data as free (e.g., and deleting a directory entry associated with the data); and/or (3) any other suitable technique for deleting the personal data.
  • the system uses an appropriate data model (see discussion above) to efficiently determine where all of the data subject's personal data is stored.
  • Figures 30-31 depict exemplary screen displays that a user may view when submitting a data subject access request.
  • a website 30000 associated with a particular organization may include a user-selectable indicia 3005 for submitting a privacy-related request.
  • a user desiring to make such a request may select the indicia 3005 in order to initiate the data subject access request process.
  • Figure 31 depicts an exemplary data subject access request form in both an unfilled and filled out state.
  • the system may prompt a user to provide information such as, for example: (1) what type of requestor the user is (e.g., employee, customer, etc.); (2) what the request involves (e.g., requesting info, opting out, deleting data, updating data, etc.); (3) first name; (4) last name; (5) email address; (6) telephone number; (7) home address; and/or (8) one or more details associated with the request.
  • information such as, for example: (1) what type of requestor the user is (e.g., employee, customer, etc.); (2) what the request involves (e.g., requesting info, opting out, deleting data, updating data, etc.); (3) first name; (4) last name; (5) email address; (6) telephone number; (7) home address; and/or (8) one or more details associated with the request.
  • a data subject may submit a subject access request, for example, to request a listing of any personal information that a particular organization is currently storing regarding the data subject, to request that the personal data be deleted, to opt out of allowing the organization to process the personal data, etc.
  • the questionnaire template generation system and assessment system described herein may incorporate one or more risk flagging systems.
  • Figures 32-35 depict exemplary user interfaces that include risk flagging of particular questions within a processing activity assessment.
  • a user may select a flag risk indicia to provide input related to a description of risks and mitigation of a risk posed by one or more inventory attributes associated with the question.
  • the system may be configured to substantially automatically assign a risk to a particular response to a question in a questionnaire.
  • the assigned risk is determined based at least in part on the template from which the assessment was generated.

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Abstract

Des modes de réalisation particuliers de la présente invention concernent un système de génération d'inventaire de données de traitement de données qui est conçu pour : (1) générer un modèle de données (par exemple un inventaire de données) pour un ou plusieurs actifs de données utilisés par une organisation particulière ; (2) générer un inventaire de données respectif pour chacun desdits actifs de données ; et (3) mettre en correspondance une ou plusieurs relations entre un ou plusieurs aspects de l'inventaire de données, lesdits actifs de données, etc. au sien du modèle de données. Dans des modes de réalisation particuliers, un actif de données (par exemple un système de données, une application logicielle, etc.) peut comprendre, par exemple, toute entité qui collecte, traite, contient et/ou transfère des données personnelles (par exemple une application logicielle, un dispositif informatisé "Internet des objets", une base de données, un site Web, un centre de données, un serveur, etc.). Un premier actif de données, par exemple, peut comprendre n'importe quel logiciel ou dispositif (par exemple un serveur ou des serveurs) utilisé par une entité particulière pour une telle collecte de données, un traitement, un transfert, un stockage, etc.
PCT/US2018/043976 2017-07-27 2018-07-26 Systèmes de traitement de données pour générer et charger un inventaire de données WO2019023510A1 (fr)

Applications Claiming Priority (12)

Application Number Priority Date Filing Date Title
US201762537839P 2017-07-27 2017-07-27
US62/537,839 2017-07-27
US15/996,208 2018-06-01
US15/996,208 US10181051B2 (en) 2016-06-10 2018-06-01 Data processing systems for generating and populating a data inventory for processing data access requests
US16/041,563 2018-07-20
US16/041,520 2018-07-20
US16/041,468 2018-07-20
US16/041,520 US10275614B2 (en) 2016-06-10 2018-07-20 Data processing systems for generating and populating a data inventory
US16/041,545 2018-07-20
US16/041,563 US10282700B2 (en) 2016-06-10 2018-07-20 Data processing systems for generating and populating a data inventory
US16/041,545 US10204154B2 (en) 2016-06-10 2018-07-20 Data processing systems for generating and populating a data inventory
US16/041,468 US10284604B2 (en) 2016-06-10 2018-07-20 Data processing and scanning systems for generating and populating a data inventory

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PCT/US2018/043977 WO2019023511A1 (fr) 2017-07-27 2018-07-26 Systèmes de traitement de données permettant de générer et de remplir un stock de données
PCT/US2018/043976 WO2019023510A1 (fr) 2017-07-27 2018-07-26 Systèmes de traitement de données pour générer et charger un inventaire de données
PCT/US2018/043975 WO2019023509A1 (fr) 2017-07-27 2018-07-26 Systèmes de traitement et de balayage de données pour générer et alimenter un inventaire de données
PCT/US2018/044020 WO2019023534A1 (fr) 2017-07-27 2018-07-27 Systèmes de traitement de données pour générer et alimenter un inventaire de données pour traiter des demandes d'accès à des données
PCT/US2018/044026 WO2019023538A1 (fr) 2017-07-27 2018-07-27 Systèmes de traitement de données permettant de générer et garnir un inventaire de données
PCT/US2018/044046 WO2019023550A1 (fr) 2017-07-27 2018-07-27 Systèmes de traitement de données pour générer et peupler un inventaire de données afin de traiter des requêtes d'accès à des données

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PCT/US2018/044020 WO2019023534A1 (fr) 2017-07-27 2018-07-27 Systèmes de traitement de données pour générer et alimenter un inventaire de données pour traiter des demandes d'accès à des données
PCT/US2018/044026 WO2019023538A1 (fr) 2017-07-27 2018-07-27 Systèmes de traitement de données permettant de générer et garnir un inventaire de données
PCT/US2018/044046 WO2019023550A1 (fr) 2017-07-27 2018-07-27 Systèmes de traitement de données pour générer et peupler un inventaire de données afin de traiter des requêtes d'accès à des données

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WO2019023534A1 (fr) 2019-01-31
WO2019023550A1 (fr) 2019-01-31
WO2019023509A1 (fr) 2019-01-31
WO2019023538A1 (fr) 2019-01-31

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