GENERATING DATA COMPLIANCE CONSTRAINTS

In one embodiment, a device may extract, from one or more bodies of text, a data usage restriction for a particular type of data. The device may send, to a user interface, the data usage restriction extracted from the one or more bodies of text for presentation for a user. The device may receive, via the user interface, feedback from the user regarding the data usage restriction. The device may generate a data compliance constraint that controls how an application service handles the particular type of data, based on the data usage restriction and the feedback from the user.

TECHNICAL FIELD

The present disclosure relates generally to generating data compliance constraints.

BACKGROUND

Applications operate by handling data. For instance, executing an application can involve the storage, communication, processing, etc. of various types of data. The various types of data may include data whose handling is subject to various regulations. For example, data handling regulations at national, federal, state, industry, and/or organizational levels may be applicable to the data handled by an application.

The enforcement of data compliance has only been made more complex and potential violations made more likely as applications are increasingly being developed as a set of distributed services running across a mix of multi-cloud and edge infrastructures, handling a mix of data types differentially subject to various regulations, and/or being utilized by mobile endpoints. Unfortunately, following deployment of an application, enforcement of data compliance requirements largely occurs non-specifically and in a programmatic blind-spot. Given the current regulatory environment and trends, continuing to treat data compliance as an afterthought in this manner will likely yield increased violations of data compliance regulations which may result in substantial fines, penalties, and/or other negative impacts to data handlers.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure a device may extract, from one or more bodies of text, a data usage restriction for a particular type of data. The device may send, to a user interface, the data usage restriction extracted from the one or more bodies of text for presentation for a user. The device may receive, via the user interface, feedback from the user regarding the data usage restriction. The device may generate a data compliance constraint that controls how an application service handles the particular type of data, based on the data usage restriction and the feedback from the user.

DESCRIPTION

In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/5G/LTE backup connection). For example, a particular CE router110shown in network100may support a given customer site, potentially also with a backup link, such as a wireless connection.2.) Site Type B: a site connected to the network by the CE router via two primary links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). A site of type B may itself be of different types:2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). For example, a particular customer site may be connected to network100via PE-3 and via a separate Internet connection, potentially also with a wireless backup link.2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement at all or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site).3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/5G/LTE backup link). For example, a particular customer site may include a first CE router110connected to PE-2 and a second CE router110connected to PE-3.

According to various embodiments, a software-defined WAN (SD-WAN) may be used in network100to connect local network160, local network162, and data center/cloud environment150. In general, an SD-WAN uses a software defined networking (SDN)-based approach to instantiate tunnels on top of the physical network and control routing decisions, accordingly. For example, as noted above, one tunnel may connect router CE-2 at the edge of local network160to router CE-1 at the edge of data center/cloud environment150over an MPLS or Internet-based service provider network in backbone130. Similarly, a second tunnel may also connect these routers over a 4G/5G/LTE cellular service provider network. SD-WAN techniques allow the WAN functions to be virtualized, essentially forming a virtual connection between local network160and data center/cloud environment150on top of the various underlying connections. Another feature of SD-WAN is centralized management by a supervisory service that can monitor and adjust the various connections, as needed.

FIG.3illustrates an example architecture300for data compliance, according to various embodiments. The architecture300may include a data compliance as code process328. Data compliance as code process328may be utilized to provide configuration, observability, and enforcement of data compliance rules. Data compliance as code process328may accomplish these functions utilizing Data Compliance as Code (DCaC).

DCaC may include integrating a data compliance mechanism into the program code of an application and/or application service. For example, data compliance as code process328may be utilized to build data compliance into the application development process, supported by automated code annotations, bindings between such annotations and categories of sensitive data, and controls at code, build, and pre-deploy time. Data compliance as code process328may provide a mechanism whereby application developers proactively assist data teams, application managers, and legal departments with data compliance, while ensuring that developers may remain oblivious to specific regulations, data related obligations, or compliance requirements that organizations might have across different regions.

For example, data compliance as code process328may include data annotating process302. Data annotating process302may facilitate application developer312automatically adding metadata to program code of an application316during the development of the application316. In various embodiments, this may be performed by automated annotations of data fields in the program code and by the creation of references to such annotations at code-build time. These references to annotated code may be automatically rendered in the form of machine-readable data manifest314.

More specifically, data annotating process302may provide a mechanism for automated annotations of the program code of application316, including classes, application programming interfaces (APIs), and the resulting data at code/build time (e.g., by implementing a Low-Code/No-Code approach supported by software development kits (SDKs)320and tooling318). Application developers may utilize SDKs320and tooling318to automatically label data topics, data producers, data consumers, data processors, data holders, etc. For instance, developers may label certain data by annotating it with a data type identifier. For example, a developer may annotate certain data as “protected-type-1,” or other data as “protected-type-2,” and so on.

SDKs320in data annotating process302may provide a set of predefined data types out-of-the-box, including associations by default to specific categories of sensitive data. Sensitive data may include a type of data that may be considered personal, private, sensitive, confidential, protected, secret, restricted, personally identifiable information (PII), etc. In some examples, sensitive data may include data that is subject to regulation. For example, Table 1 lists examples of predefined protected data types and default associations to some examples of categories of sensitive data.

TABLE 1PROTECTED DATA TYPEDEFAULT ASSOCIATIONprotected-type-1Customer PIIprotected-type-2Employee PII. . .. . .protected-type-23Patient Analysis Results. . .. . .protected-type-41Sales Confidential. . .. . .protected-type-56Restricted HR. . .. . .unprotectedNA
A list of the associations, such as the example illustrated in Table 1, may provide associations by default to several categories of sensitive data, including but not limited to PII, confidential, restricted, and unprotected data. In some embodiments, the set of predefined protected data types might be standardized or rely on an existing taxonomy.

SDKs320in data annotating process302may also provide a mechanism to define and use custom data types in annotating program data of the application316. For example, custom data types may be utilized, which identify protected data types that are not covered by any of those available by default in SDKs320. For example, “custom-type-1” might be a custom data type associated to a category of sensitive data such as “Restricted Employee Poll.” In various embodiments, the generation and/or insertion of the annotations into the program code of the application316may be accomplished by an automated process (e.g., a programmatic identification of data of a particular data type triggering an automated insertion of an annotation of the data as the particular data type, etc.), a partially automated process (e.g., a programmatic flagging of data of a particular data type with a supervised or manual annotation of the data as the particular data type, etc.), and/or a manual process (e.g., a manual flagging of data of a particular data type and/or a manual annotation of the data as the particular data type, etc.).

In various embodiments, associations between protected data types and categories of sensitive data may be assigned and/or instrumented by different organizations and at different moments in time. In some cases, the association between protected data types and categories of sensitive data may be assigned by application developers312at code/build time. This might be the case when the team of application developers312is part of, or develops for, the organization that may use or manage the application316. In such cases, the team of application developers312might have sufficient knowledge about the data and their use, so that they may either use the associations provided by default or create custom ones.

In additional instances, application developers312of application316and/or the users of the application316might belong to different organizations. For example, this may be the case when application developers312are a DevSecOps team that develops an application316that may be used across different organizations, industries, etc. In such cases, application developers312may be unaware of the categories of data that should be assigned by a data team and/or application manager304in another organization (e.g., precisely what data is confidential and what data is not with respect to that organization and its use of the application316). In these instances, application developers312may leverage SDKs320and tooling318to approach data labeling and association in a manner that sidesteps the knowledge deficit while still instilling the functionality. For example, the application developers312may leverage SDKs320and tooling318to automatically add the different classes of protected data type at code and build time (e.g., utilizing predefined and custom protected data types). Additionally, or alternatively, the application developers312may leverage SDKs320and tooling318to automatically insert references in the form of machine-readable descriptions for the protected data types that may be used to generate data manifest314bound to application316at build time.

The protected data type annotations and their corresponding references may be utilized by a data team and/or application manager304in another organization to select and/or create automated associations326between categories of annotated data in the application316(e.g., metadata provided by application developers312) and specific categories of sensitive data (e.g., personal data, private data, sensitive data, confidential data, protected data, secret data, restricted data, PII, etc.). For instance, each protected data type might be bonded to a class of tokens (e.g., JSON Web Tokens with a specific scope), which in turn might represent different categories of sensitive data for a data team and/or application manager304.

In a specific example, an API call for application316may be labeled by application developers312with a data type identifier such as “custom-type-7” at code/build time. The “custom-type-7” labeled API call may attempt to access certain data using its bound token (e.g., “Token 7”) with a scope defined by, for example, a data team and/or application manager304before application316was deployed. From the data team and/or application manager304perspective, the attempt to access this data may translate to a request to access, for instance, “Confidential Partner” data. As such, the data type labels, and their associations may be utilized as an automated data mapping between the programmatic operations of application316and the sensitive data implicated in those operations. In various embodiments, these associations and functionalities may be supported by compliance engine306based on the selection, configuration, and automation of data compliance rules before application316is deployed and/or post-deployment.

In some examples, application developers312, which again may be a DevSecOps team, might opt for a hybrid approach to generating these associations. For example, this may be the case when making some custom associations between data types and categories of sensitive data or using those predefined in the system (e.g., “protected-type-1” to “Customer PII”) might not only be trivial for the application developers312but also may facilitate the task of a data team and/or application manager304in defining associations. However, other associations might not be apparent to application developers312. Hence, certain data in application316may be labeled as “protected types” along with their corresponding machine-readable descriptions in data manifest314, though they may remain unassigned to a specific category of sensitive data, so they can be associated later by a data team and/or application manager304before the application is deployed, or by an automated data lineage, classification, and tagging process at run time (e.g., during the testing phase, that is, before the application is deployed in production).

In some embodiments, a data team and/or application manager304may be provided with a mechanism to change the associations created by application developers312or even associate more than one category of sensitive data to a given data type (e.g., a data team and/or application manager304may associate certain data with both “Employee PII” and “Confidential Data”). Hence, two categories of data compliance policies (e.g., one for “Employee PII” and another for “Confidential Data”) may apply and restrict even further the access to this category of data. In general, a data team and/or application manager304may be able to Create, Read, Update, or Delete (CRUD) any association between the metadata provided by application developers312and categories of sensitive data.

In various embodiments, a data team and/or application manager304may proactively create a set of custom data types. A data team and/or application manager304may provide the set of custom data types to application developers312. Application developers312may then utilize the set of custom data types so that application316is annotated at development based on guidelines (e.g., the set of custom data types, etc.) provided beforehand by the data team and/or application manager304.

In additional embodiments, application developers312and a data team and/or application manager304may collaborate to annotate application316. For example, application developers312and a data team and/or application manager304may iterate in the annotation and association processing in an agile manner. For example, the iteration may be performed as part of a continuous integration/continuous delivery (Cl/CD) pipeline (e.g., at testing, staging, and production).

In some examples, application316may be composed of several services developed with different programming languages. Therefore, application316may utilize different SDKs320. In some instances, the annotation methods and terminology applied to application316may vary depending on the programming language (e.g., usually referred to as attributes in C #, decorators in Python, annotations in Golang, etc.). In such cases, tooling318of data annotating process302may examine the different predefined and custom data types used with different SDKs320, perform checks, and ensure consistency in the annotations and enumeration across the different services at build time. For example, these consistency checks may ensure that a given “custom-type-X” data type identifier represents the same type of data across services programmed using different programming languages even if they were programmed by different developers. Overall, the data annotating process302may provide different degrees of freedom to application developers312, data teams and/or application managers304, and the number of protected data types used, and their corresponding associations may vary depending on the type of application316.

Data annotating process302may, as described above, be utilized in generating automated data references. Specifically, data annotating process302may automatically render a data manifest314bonded to application316at build time. Data manifest314may provide machine-readable descriptions of the predefined and/or custom data types used by application316. A combination of SDKs320and tooling318may facilitate the instrumentation and automation of the program code at build time, including the automated rendering of data manifest314. In some cases, application316may be composed of various containers. Each container may be built and packaged with its own data manifest, such that the final data manifest rendered for application316may be a composition of the individual data manifests. In some cases, application316may include dependencies on external services, such as a MySQL database. Such dependencies may be captured as a dependency manifest. Data fed, processed, retained, or retrieved from these external services may also be annotated and automatically captured in application316data manifest314.

Data annotating process302may, as described above, be utilized for decoupling data compliance from the business logic of application316. For example, SDKs320and tooling318of data annotating process302may provide automated mechanisms for decoupling the configuration, observability, and enforcement of data compliance rules from the business logic of application316. In some instances, application316may be a cloud/edge native application, which may be implemented as a set of workloads composing a service mesh. The decoupling of data compliance from the business logic may be especially relevant for applications of this type, as geographically dispersed and/or variably deployed workloads may implicate increased data compliance complexity.

Various possible embodiments for decoupling data compliance from the business logic of application316may be utilized. For instance, a sidecar model, where the services that implement the business logic of application316are deployed together with sidecar proxies associated to each of those services, may be utilized. The sidecar proxies may be utilized to enforce horizontal functions that are independent of the business logic, such as routing, security, load balancing, telemetry, retries, etc. As such, the sidecars may be well-positioned to decouple, observe, and control data compliance. For example, a combination of distributed data compliance controllers and sidecar proxies may be used to configure, observe, and enforce data compliance rules across different geographies, and distributed multi-cloud and edge infrastructures334.

Instead of, or in addition to, using sidecars, various embodiments may use client libraries, daemons working in tandem with the application-specific services, or sandboxed programs in the OS kernel, e.g., using the Extended Berkeley Packet Filter (eBPF). Further embodiments may use an agentless approach or embed such functionality in Kubernetes itself. In any case, the functionality introduced herein may enable the portability and reuse of observability and enforcement of data compliance functions across not only different applications but also cloud and edge environments.

The above-described data annotating process302may yield a portable annotated application316that is geared with built-in annotations for different types of protected data. In addition, the yielded annotated application316may be structured to operate while remaining agnostic of any state, country, industry, organization-specific regulation and/or data policy requirements that a data team and/or application manager304might have. As a result, data annotating process302may be leveraged as a new model of building applications including DCaC by not only data teams and/or application managers304, but also software as a service (SaaS) providers and others.

Data compliance as code process328may provide configuration, observability, and enforcement of data compliance rules. As described above, associations326between categories of annotated data in application316and specific categories of sensitive data may be instrumented prior to a deployment of application316. The associations326may be used to control the processing and use of data during and after the deployment of application316. More specifically, compliance engine306may utilize associations326together with current data compliance regulations governing data handling in each region where application316may be used, as well as a specific organization's compliance rules308for/while using application316, to enforce compliance with them. Such controls may apply to data access requests, data storage and retention policies, data processing requirements, etc. of application316both at deploy and execution time, etc.

To this end, data compliance as code process328may include data compliance rule repository322. Data compliance rule repository322may provide a repository of data compliance rules. A data compliance rule may include a data usage restriction such as an industry/company/legal standard, an industry/company/legal regulation, an industry/company/legal preference, an industry/company/legal policy, an industry/company/legal obligation (contractual or otherwise), a statute, a court ruling, and/or any other expression of an industry/company/legal data usage restriction. The data usage restriction may be geography-specific, jurisdiction-specific, company-specific, client-specific, industry-specific, data type-specific, etc.

For example, data compliance rules repository322may include a repository of industry rules324which may be applicable to the use of application316within a specific industry. For example, with respect to instances where application316is used by a healthcare provider, data compliance rule repository322may include industry rules324such as Health Insurance Portability and Accountability Act of 1996 (HIPAA) regulations applicable to handling of data in the healthcare industry. In other examples, data compliance rule repository322may include a repository of geographic rules330which may be applicable to the use of application316within a specific geographical location. For example, with respect to instances where application316is based in a member state of the E.U., data compliance rules repository322may include geographic rules330such as the GDPR applicable to handling of data in the E.U.

The data compliance regulations included in data compliance rules repository322may be consumed by a data team and/or application manager304as a service (aaS). Data compliance rules repository322may support input, expression, collection, approval, visualization, and/or use of data compliance policies covering multiple categories of rules. The data compliance policies may include restrictions to the use of particular types of data. For example, data compliance rules repository322may store data compliance policies that are specific to an industry, those that may apply at a national, multi-national, federal, state, and industry levels, etc. For instance, an organization (e.g., a multi-national company) may leverage a data compliance rules repository322service of a data compliance as code process328and utilize the rules already available in data compliance rules repository322, which may cover regulations across several industries and countries out-of-the-box. An organization may select the target state, country or region, the industry if needed, and select the data compliance regulations that may be applicable at the organizational level (e.g., organization's compliance rules308).

Compliance engine306may offer APIs and a user-friendly user interface (UI) through which a data team and/or application manager304may select and define data compliance requirements. For instance, if application316, which handles Customer PII data, needs to be deployed in British Columbia, Canada, a data team and/or application manager304may simply select “Customer PII→Apply Local Rules” to constrain the processing, storage, retention, and access to Customer PII data according to the regulations in British Columbia as retrieved from data compliance rules repository322. To this end, compliance engine306may compute and handle the resulting constraints that apply to Customer PII data in British Columbia transparently to data teams and/or application managers304. More specifically, the set of data compliance constraints may be captured in a machine-readable format from data compliance regulation repository322, and therefore, used by compliance engine306programmatically.

In some examples, compliance engine306may be utilized as a pluggable module working in tandem with one or more workload engines332, such as Cisco Intersight, Hashicorp Nomad, or other automation tools offered by cloud and edge solution providers. Workload engines332may manage the deployment of application316, subject to the rules and constraints provided by compliance engine306.

In various embodiments, compliance engine306may operate either in a push or a pull model. For instance, in a pull model, a workload engine332may receive a request to deploy application316in a given region (e.g., a request from a site reliability engineering (SRE) and/or information technology (IT) team310). In such a case, workload engine332may issue a request to compliance engine306, to compute and return data compliance rules and constraints that must be applied for their specific deployment. Alternatively, in a push model, a data team and/or application manager304may select the compliance rules required and a declarative intent for application deployment may be issued from compliance engine306to one or more workload engines332. Such deployments may involve multi-cluster service meshes, which may run across multi-cloud and edge infrastructures334. In various embodiments, the above-described sidecar proxies in the service mesh may not only provide monitoring and observability of data compliance to compliance engine306but also may receive configuration and compliance updates in real-time336. In additional embodiments, the same functionality may be implemented utilizing client libraries, daemons, eBPF, an agentless approach, or Kubernetes itself. In addition, some embodiments may support the techniques described herein without utilizing a service mesh.

As noted above, the number and scope of laws and regulations regarding the processing, storage, and use of certain types of data are continually increasing across the world. For instance, the General Data Protection Regulation (GDPR) in Europe places strict requirements on how a user's personal data is collected and processed throughout its lifecycle. The GDPR has extraterritorial reach (e.g., it applies to entities outside of the EU) and includes broad definitions of personal data and equally broad definitions of processing activities. These and other requirements have spawned independent efforts across several countries to ensure that modern applications comply with specific data regulation at national, federal, or state level, particularly, those that are cloud delivered.

Legal uncertainty in the eye of accelerated digital transformation has resulted in concerns around cross-border data transfers and has given rise to “data localization” trends including its most severe form “data sovereignty”. This dynamic is posing complex challenges to the organizations that process and manage the application data, since legal obligations and constraints often vary from country to country. The challenge is even greater since data compliance requirements are usually not limited to data sovereignty obligations.

Depending on the type of application, data compliance may involve the amalgamation of other requirements. For example, data compliance may involve industry-specific rules (e.g., complying with HIPAA obligations in the healthcare industry in the US), or organization-specific rules (e.g., on how to handle confidential information, i.e., data that might not be regulated by the law).

Similarities in terminology and semantic relatedness may exist between the various concepts that are subject to data protection and/or other data regulations across the world. However, determining the set of data compliance constraints that must be applied when application data is jointly subject to data sovereignty obligations, industry-specific regulation, and organization-specific rules remains a complex, manual, and labor-intensive task.

For instance, data teams in multinational corporations may work with legal counsel to constantly interpret any relevant regulation or gain specialized expertise on the resulting constraints. The collaborative interpretations may be relied upon to continuously keep such knowledge up-to-date as new regulations arise or existing ones are updated. This costly and labor-intensive approach to keeping abreast of regulatory landscapes often fails to produce a reliable regulatory knowledgebase as the volume of information to be processed and diversity of expertise, knowledge, and organizational familiarity involved in building and modifying a holistic regulatory knowledgebase may be outside the capacity of an individual or team.

Generating Data Compliance Constraints

The techniques herein introduce mechanisms to programmatically extract data usage (e.g., sending, receiving, storing, processing, transforming, etc.) restrictions from various possible sources, along with the automated inference of actionable data compliance rules for applications subject to data sovereignty and other compliance rules, which may apply across different geographical locations, multi-cloud, and edge. These techniques may utilize commonalities, wide reuse of terms, and semantic relatedness between various concepts that are subject to data protection and regulations across different regions and industries.

Specifically, according to various embodiments, a device may extract, from one or more bodies of text, a data usage restriction for a particular type of data. The device may send, to a user interface, the data usage restriction extracted from the one or more bodies of text for presentation for a user. The device may receive, via the user interface, feedback from the user regarding the data usage restriction. The device may generate a data compliance constraint that controls how an application service handles the particular type of data, based on the data usage restriction and the feedback from the user.

Operationally, and according to various embodiments, the techniques described herein may be utilized to extract information programmatically from data usage restrictions and turn such knowledge into actionable constraints that workload engines and/or other systems can parse and use.

FIG.4illustrates an example architecture400for extraction of data usage restrictions and generation of data compliance constraints, in accordance with one or more embodiments described herein. The architecture400may include a data compliance as code (DCaC) process328. As described above, DCaC process328may include data compliance rule repository322supporting compliance engine306in programmatically generating data compliance constraints to be applied to workload engines332in spinning up clusters supporting application workloads408across multi-cloud and/or edge infrastructures334.

As described above, compliance engine306may enable data team and/or application manager304to enter and/or manage data compliance requirements of an organization. For instance, if an application that handles Personally Identifiable Information (PII) needs to be deployed in Brazil, data team and/or application manager304may simply select “PII→Apply Local Regulation”, and compliance engine306may compute and generate the resulting constraints that apply to PII in Brazil transparently to data team and/or application manager304. More specifically, the set of data compliance constraints may be captured in a machine-readable manner, and therefore, may be used by compliance engine306programmatically.

Data compliance rule repository322may provide the repository of rules, both in machine-readable and human-readable formats, which may be consumed by data team and/or application manager304, legal counselors, external systems as a service (aaS), etc. through DCaC process328. In addition to any rules already available in the data compliance rule repository322, which may cover regulations across several industries and/or geographic locations out-of-the-box, data compliance rule repository322may be populated with data compliance rules (e.g., data usage restrictions) programmatically extracted from various sources.

In various embodiments, a data usage restriction extraction service402may programmatically process data usage restrictions and/or other regulatory data from a variety of sources. For example, data usage restriction extraction service402may programmatically process any of a variety of expressions of data usage restrictions406. Expressions of data usage restrictions406may include records of data usage restrictions (e.g., rules, regulations, statutes, laws, treaties, legislative records, court decisions, guidelines, policies, preferences, best-practices, contracts, communications, obligations, etc.). The records may include audio records, visual records, text records, digital records, documents, files, posts, etc. In some examples, the records may include bodies of text such as words, sentences, paragraphs, pages, volumes, etc.

For example, expressions of data usage restrictions406may, in some examples, be industry-specific regulation documents (e.g., expression of data usage restriction406d). An industry-specific regulation may include a regulation specific to industries such as third-party data suppliers, education institutions, financial services, medical/healthcare institutions, energy/utility providers, etc.

Expressions of data usage restrictions406may include data type-specific regulation documents (e.g., expression of data usage restriction406c). For example, data type-specific regulation documents may include documentation of regulations covering the handling of personal data. A personal data regulation document may include documentation of data usage restrictions (e.g., GDPR, CCPA, an omnibus personal data regulation document, etc.) for handling personally identifiable information (PII) and/or other personally distinguishable sensitive data.

In addition to being specific to an industry or data type, expressions of data usage restrictions406may be geolocation specific. For example, expressions of data usage restrictions406may be specific to an industry within a geographic location, specific to using a data type within a geographic location, etc.

Expressions of data usage restrictions406may, in some examples, be ontology-based online forms (e.g., expression of data usage restriction406b). The online forms may capture industry specific, data type specific, etc. data usage restrictions.

Additionally, or alternatively, expressions of data usage restrictions406may, in some examples, be an ontology (e.g., expression of data usage restriction406a) capturing industry specific, data type specific restrictions, etc. data usage restrictions.

Data usage restriction extraction service402may extract the corresponding data usage restrictions from expressions of data usage restrictions406. For example, data usage restriction extraction service402may utilize various data sources and programmatic techniques which may employ various statistical and/or non-statistical mechanisms of data extraction to extract the data usage restrictions.

Data usage restriction extraction service402may store the extracted data usage restrictions (e.g., as digests of extracted data usage restrictions) in a human-readable format and/or in a machine-readable format. The data usage restriction digests may be stored in data compliance rule repository322.

The data usage restriction digests stored in data compliance rule repository322may be utilized, in addition to other compliance rules that might be required at the organizational level (e.g., organization's compliance rules308), by workload engines332to deploy an application service. For example, compliance engine306may be utilized as a pluggable module working in tandem with workload engines332, such as Cisco Intersight, Hashicorp Nomad, or other automation tools offered by cloud and edge solution providers. These workload engines332may manage the deployment of the application service, subject to the rules and constraints provided by compliance engine306.

Compliance engine306might operate either in a push or pull model. For instance, in a pull model, workload engines332may receive a request to deploy an application in a given geographic region (e.g., from the SRE or IT team310). In such examples, workload engines332may issue a request to compliance engine306to compute and return the data compliance constraints that must be applied for that specific deployment.

The inference and response of such data compliance constraints in an actionable manner may be supported by a data compliance constraint inference service404of compliance engine306. Alternatively, in a push model a data team and/or application manager304may select the compliance rules required, and a declarative intent for deploying the application might be issued from compliance engine306to one or more of workload engines332. If the application deployment performed by workload engines332is successful, this may result in the spin up of several clusters (e.g., a set of K8s or K3s clusters) supporting the application workloads408across multi-cloud and edge infrastructures334.

FIG.5illustrates another example architecture500for extraction of data usage restrictions and generation of data compliance constraints. For example, architecture500may be utilized to enable a programmatic extraction of data usage restrictions from various sources (e.g., expressions of data usage restrictions406) and their storage in data compliance rule repository322. The programmatic extraction of data usage restrictions may be performed utilizing statistical and/or non-statistical information extraction techniques.

In various embodiments, such as those employing statistical techniques to extract data usage restrictions, data usage restriction extraction service402may receive expressions of data usage restrictions406as inputs. The expressions of data usage restrictions406may include an industry-specific regulation document (e.g., expression of data usage restriction406d) and/or a data type-specific regulation document (e.g., expression of data usage restriction406c).

Data usage restriction extraction service402may process each of the expressions of data usage restrictions406independently. For each of the expressions of data usage restrictions406, the resulting output may include one or more digest of data usage restrictions (e.g., data usage restriction digest502) extracted from the expressions of data usage restrictions406. For example, the output for expressions of data usage restrictions406may include a data usage restriction digest in a human-readable format (e.g., human-readable data usage restriction digest502a) and/or a data usage restriction digest in a machine-readable format (e.g., machine-readable data usage restriction digest502b).

Each data usage restriction digest502may include descriptions of data usage restrictions such as those extracted from the expressions of data usage restrictions406. Data usage restriction digest502may be specific to and/or include data usage restrictions applicable to a specific geolocation and/or industry of an application service. Data usage restriction extraction service402may generate as many data usage restriction digest502as needed depending on the geolocation and/or industries of interest (e.g., the geographic location where an application service will operate and/or an industry associated with the application service).

Data usage restriction extraction service402may include information extraction service508. Information extraction service508may include language normalization module510. Language normalization module510may translate and bring different data usage restrictions from the expressions of data usage restrictions406to a common language. However, some embodiments may skip this step and/or exclude this module, instead relying on external means to achieve this translation. For instance, information extraction service508may be fed data usage restrictions in a common language (e.g., English).

Data usage restriction extraction service402may utilize natural language processing (NLP) techniques (e.g., executed by NLP module512). NLP techniques may be used to perform natural language processing and transformation of the expressions of data usage restrictions406. NLP techniques may be utilized to summarize and/or reformulate expressions of data usage restrictions406(e.g., one or more bodies of text therein) by leveraging one or more dictionaries514.

Dictionaries514may capture and/or define the reference terminology and/or domain-specific lexicon of the subject expressions of data usage restrictions406. Dictionaries may store relevant definitions on data regulation at state, province, country, multi-national, and industry-specific levels to access them from data structure522.

A combination of inference process516and disambiguation process518may utilize a set of domain-specific data compliance ontologies (e.g., data compliance domain ontologies524) and use techniques such as lexical matching, clustering, and semantic relatedness to identify the key concepts and relationships between such concepts in expressions of data usage restrictions406. The pool of data compliance domain ontologies524may include one reference ontology for data protection regulation of a particular geographic area (e.g., at national level, state level, city level, etc.), another ontology for data regulation in a particular industry (e.g., the finance industry), another for data regulation in another industry (e.g., the healthcare industry), etc. These reference ontologies may generalize and/or comprehensively capture the set of concepts and possible relationships that may apply among such concepts for each knowledge domain, independently of any specific data usage restriction in that domain.

Such ontologies may comprise multiple layers and taxonomies, such as a “data types” or “data categories” layer, a “data actors” layer, a “data resource” layer, a “data operations” layer, a “data permissions” layer, a “data location” layer, etc. For example, in one ontology a concept in the “data types” or “data categories” layer (e.g., “PII”), might be semantically related to a concept in the “data operations” layer (e.g., “Data Processing”) through the property “CONSTRAINED TO”. Likewise, the concept “Data Processing” in the “data operations” layer might be semantically related to a concept in the “data location” layer (e.g., “Country”) through the property “WITHIN”. Hence, once possible set of relationships among concepts captured by the ontology could be: (PII)→CONSTRAINED TO→(Data Processing)→WITHIN→(Country). Mapping function520may identify and/or map the concepts and relationships that would be obtained from a specific data usage restriction that is being processed by the information extraction service508to those that semantically match in the reference (domain-specific) ontology (e.g., the corresponding ontology from data compliance domain ontologies524).

Data usage restriction extraction service402may include data usage restriction generator526. Data usage restriction generator526may utilize the outputs (e.g., from mapping function520) of information extraction service508to generate an ontology specific to a data usage restriction being processed and/or to generate digests of data usage restrictions (e.g., data usage restriction digest502). For example, data usage restriction generator526may render both human-readable data usage restriction digest502aand machine-readable data usage restriction digest502b.

For example, the mapping yield by mapping function520may be subsequently used by restriction-specific ontology generator528to prune the reference ontology (e.g., the corresponding ontology from data compliance domain ontologies524) and produce an ontology that models the specific data usage restriction being processed by information extraction service508. Each of the ontologies generated by restriction-specific ontology generator528might be represented as a sub-graph of the reference (domain-specific) ontology (e.g., data compliance domain ontologies524). Hence, the newly generated ontology may capture the knowledge about the specific data usage restrictions contained in the expression of data usage restrictions406that is being processed.

The ontology computed by the restriction-specific ontology generator528may serve as an input to a restriction digest generator530. Restriction digest generator530may render both human-readable data usage restriction digest502aand machine-readable data usage restriction digest502bfrom the data usage restrictions extracted from expressions of data usage restrictions406. Data usage restriction digests502may include descriptions of and/or instructions for implementing their data usage restrictions as a constraint to a particular data type.

The techniques above described may utilize commonalities and semantic relatedness between the various concepts that are subject to data protection and regulations across different regions and industries. For example, GDPR is widely considered as a benchmark data handling regulation, so many of the existing data usage restrictions and upcoming ones may be inspired by a common set of principles within the GDPR. Indeed, many of them leverage the taxonomies, terminology, lexical semantics, and rules (or variants of them) present in GDPR. Similarly, industry-specific data usage restrictions also have clear commonalities across different regions. For instance, this data could be utilized as the basis to populate data structure522with reference terminology and data usage restriction data.

In addition, the solution space for data compliance constraints may be discrete, bounded, and sparse. For instance, a considerable number of data localization restrictions may be clustered into three groups regarding personal data: G1) those where data can be processed, stored, and accessed both within and outside the region of interest; G2) those where data must be stored within the region of interest, but they can be processed and accessed both within and outside the region of interest under certain data retention conditions; and/or G3) those where data must be processed, stored, and accessed exclusively within the region of interest. These characteristics may facilitate considerably the tasks of programmatically searching for and extracting data usage restrictions from expressions of data usage restrictions406and generating the corresponding data usage restriction digest502.

Data usage restriction extraction service402may include audit service536. Audit service536may operate in a semi-autonomous and/or autonomous mode. Audit service536may elicit and/or receive feedback from a user regarding the data usage restriction digest502generated from the extracted data usage restrictions.

For example, audit service536may send a human-readable data usage restriction digest502ato a user for their review and/or provision of feedback. The feedback may include approval of data usage restriction digest502, rejection of data usage restriction digest502, modification of data usage restriction digest502, removal of data usage restriction digest502, further restriction of data usage restriction digest502, lessening restriction of data usage restriction digest502, commenting on data usage restriction digest502, etc.

The feedback may be utilized to revise the data usage restriction digest502. For example, the feedback may be utilized to steer specific modifications (e.g., more restrictive, less restrictive, more inclusive, less inclusive, etc.) to the data usage restriction digest502and/or to approve them before they are stored in data compliance rule repository322.

When operating in a semi-autonomous mode, audit service536may send at least the human-readable data usage restriction digest502ato a user for the user to provide feedback at532. For example, audit service536may require a user such as an authorized legal counselor to approve the data usage restriction digest502generated by data usage restriction generator526and/or to supervise and/or request specific refinements to the obligations identified (e.g., via directed search and refinement module534).

As previously noted, data compliance rule repository322may provide a comprehensive catalog of data usage restriction digests for different geographical locations, capturing both personal data and industry specific obligations selectively employable as data compliance constraints to application services. Since the resulting data usage restriction digests stored in data compliance rule repository322may be uniformly formatted, they may be available and delivered in machine-readable format. Therefore, the resulting data usage restriction digests may be inserted into data compliance rule repository322and consumed and/or used programmatically by compliance engine306or third-party systems. For example, the resulting rule digests and/or their component data usage restrictions may be consumable in machine-readable format as a service from data compliance rule repository322. Thus, compliance engine306may pull a particular data usage restriction from data compliance rule repository322and utilize it as a data compliance constraint to be applied (e.g., via a workload engine332) to the operation of an application workload408.

In some examples, audit service536may automatically find data usage restriction digests already present in data compliance rule repository322from the same and/or similar geographies, industries, etc. and compare their classifications to the classifications of the data usage restriction digests502. For example, audit service536may determine that the same and/or similar data usage restriction digests already present in data compliance rule repository322are either classified differently from the machine-readable data usage restriction digest502b, constituting a conflicting classification, and triggering a reconciliation process which may involve notifying a user of the conflict and requesting additional feedback. Alternatively, audit service536may determine that the same and/or similar data usage restriction digests already present in data compliance rule repository322are either classified similarly or identically to machine-readable data usage restriction digest502b, constituting a confirmation of the classification which may be utilized as permission to save the machine-readable data usage restriction digest502bto data compliance rule repository322and/or to make it consumable therefrom as a data compliance constraint.

Various embodiments may include additional instrumentation and/or enforcement refinements to the data usage restrictions identified by information extraction service508and/or data usage restriction generator526. For instance, audit service536may enable a user to edit the human-readable digest502aand/or modify specific fields thereof. These modifications may be fed back (e.g., via directed search and refinement module534) to information extraction service508and/or data usage restriction generator526. The modifications may be utilized not only to regenerate data usage restriction digest502but also to train and/or refine information extraction service508in a semi-supervised fashion.

When operating in an autonomous mode, audit service536may operate as a combination of algorithmic and machine learning models that may fully automate the techniques of data usage restriction extraction service402. For instance, audit service536may be fully automated to programmatically and/or autonomously review the machine-readable data usage restriction digest502b. For instance, audit service536may be trained to detect deviations from a set of bounded outcomes (e.g., digests cannot confidently fit into any of the groups G1, G2 and G3 described above) and/or to find replications or contradictions in the digests (e.g., similar to finding conflicting or confirming classifications among existing restriction digests in data compliance rule repository322, as described above). In such cases, a directed search and refinement module534may be triggered, enabling closed-loop operation and automated control between information extraction service508, data usage restriction generator526, and/or audit service536until a viable set (e.g., a non-conflicting and/or confirmed set) of data usage restrictions is found. Additional embodiments may rely on fully self-supervised training methods and backward feedback between modules until a viable set of obligations is found.

In contrast to the embodiments employing statistical techniques to extract data usage restrictions, various embodiments may alternatively, or additionally, employ deterministic (e.g., non-statistical) techniques to extract data usage restrictions from expressions of data usage restrictions406. For instance, various embodiments may utilize the data compliance domain ontologies524themselves (i.e., the set of reference ontologies) along with non-statistical techniques to simplify the data usage restriction extraction service402.

For instance, a user, such as an authorized legal counselor, may enter the data usage restrictions directly in the data usage restriction extraction service402by filling in an online form (e.g., expression of data usage restriction406b). The form or forms that the user fills in may be internally structured according to the corresponding reference (domain-specific) ontology (e.g., an ontology from data compliance domain ontologies524). Hence, once a form is duly completed and submitted, restriction-specific ontology generator528may directly process the corresponding fields and data entered, and automatically generate a regulation-specific ontology without requiring any statistical inference. More specifically, data usage restriction extraction service402may determine the rules deterministically, instead of statistically. Information extraction service508may still be used to prefill the form and/or to provide suggestions to assist and simplify the data entry tasks involved in manually completing such forms.

In some embodiments, information extraction service508may be simplified and reduced to only NLP module512. NLP module512may utilize Pre-trained Language Models (PLMs), which may process the expressions of data usage restrictions (e.g., expression of data usage restriction406cand expression of data usage restriction406d) directly. In various embodiments, the outputs of this processing by NLP module512may be used to generate pre-filled forms (e.g., expression of data usage restriction406b), which may be ready to be completed and reviewed by users, such as authorized legal counselors.

Additional embodiments may enable a Regulatory Entity (RE) or an authorized organization to proactively provide a corresponding ontology (e.g., expression of data usage restriction406a) jointly with the expression of data usage restriction (e.g., expression of data usage restriction406c, expression of data usage restriction406d, etc.) that it is to be utilized to extract the data usage restrictions from. Such ontology may capture, in machine-readable format, the data compliance obligations in a specific data usage restriction. In addition, the ontology may be cryptographically signed by the RE and may be directly processable by data usage restriction generator526.

Irrespective of whether the data usage restrictions were captured and conveyed via an ontology-based form (e.g., expression of data usage restriction406b) or an ontology (e.g., expression of data usage restriction406a) directly, data usage restriction extraction service402may be streamlined to solely data usage restriction generator526and a streamlined version of audit service536. Hence, data usage restriction extraction service402may be utilized for programmatic extraction of data usage restrictions and their corresponding storage in data compliance rule repository322, by combining both statistical and non-statistical information extraction techniques. Those skilled in the art would understand that the described use of forms and/or ontologies simply represent examples of possible embodiments. Alternative methods, including data models, files, or protocols could be used as well.

FIG.6illustrates an example architecture600executing automated inference and generation of data compliance constraints630, in accordance with one or more embodiments described herein. Data compliance engine306may leverage the data compliance rule repository322to perform an automate inference of a data compliance constraint630(e.g., in day +1).

At610, a data team and/or application manager304may submit a request for a deployment of an application service to data compliance engine306. For example, once the data usage restrictions are generated and stored in data compliance rule repository322(e.g., in day −1), an organization may provide their data compliance requirements to data compliance engine306and let data compliance constraint inference service404automatically compute the corresponding data compliance constraints630applicable to a deployment of an application service. For instance, a data team and/or application manager304may enter a set of data compliance requirements602for a given application deployment. The set of data compliance requirements602may include the geographical location604where the application needs to be deployed, the industry608in which the application will operate, and/or a set of rules that the organization might require (e.g., organizations' compliance rules308), including data processing, storage, retention, and access policies.

At612, the request may be processed by data compliance selection module614of data compliance engine306. Data compliance selection module614may identify the geographical location and the corresponding industry of the requested application service deployment. Data compliance selection module614may use these identified characteristics as inputs to retrieve the data usage restrictions applicable to the requested deployment. For example, data compliance selection module614may identify machine-readable restriction digests and/or human-readable restriction digests stored within data compliance repository322that restrict data usage having characteristics that correspond to and/or match the identified characteristics of the requested application service.

At615, a user, such as a legal representative of an organization requesting a deployment of an application service, may revise the identified human-readable data usage restriction digests. For example, the user may approve or edit the identified human-readable restriction digests and the system may automatically update the corresponding machine-readable restriction digests to further restrict the data usage restrictions (e.g., to reduce the exposure and potential risk of the organization to future fines in case of a regulation infringement). For instance, while the identified machine-readable restriction digests and/or human-readable restriction digests may stipulate that certain data types must be stored within the region of interest, but they can be processed and accessed both within and outside the region of interest under certain data retention conditions (G2), a user may restrict this even further, and recommend to process, store, retain, and access the data exclusively within the region of interest (G3).

At616, the revised and/or approved restriction digests618output from data compliance selection module614may be caused to be stored by data compliance selection module614. The revised and/or approved restriction digests618may be stored both in a machine-readable format (e.g., machine-readable restriction digest618b) and/or in a human-readable format (e.g., restriction digest618a). The revised and/or approved restriction digests618may be those directly obtained from data compliance rule repository322or may represent a more restrictive or less restrictive version of those obtained from data compliance rule repository322.

At620, the machine-readable data usage restrictions specified in machine-readable restriction digest618bmay be used, at620a, by compute data compliance constraints module622, together with the organization's data compliance rules308retrieved from the set of data compliance requirements602, at620b, to compute the specific set of data compliance constraints630that should be applied to the application service. For instance, for “Personal Data”, such data compliance constraints630may stipulate that the processing, storage, retention, and access of this category of data must be restricted to “within the country”.

At624, compliance engine306may have access to a catalog of candidate infrastructures that might be used for the application deployment (e.g., a pool of public cloud zones, a pool of public edge zones, a set of private cloud infrastructures, etc.). Such information may be accessible as part of a “pull” request coming from a workload engine332, or it may be configured beforehand by an SRE/IT team310, or it may be available through other means. In any case, the data compliance constraints630computed at620may be mapped to the available infrastructure to filter and identify those that are compliant, and subsequently generate a declarative intent to deploy the application service.

At626, the declarative intent computed at624may be captured as a data compliance constraint630. Data compliance constraint630may be included in an actionable element628. Actionable element628may be a data manifest or other object, file, document, etc. including one or more data usage restriction. Actionable element628may be programmatically used by a workload engine332to enforce a data compliance constraint630against the application workloads whose deployment and/or execution they are managing. Enforcing the data compliance constraint630may include controlling, according to the data compliance constraint630, how the application service handles a particular type of data specified in the data compliance constraint. For example, if the data compliance constraint630includes a data usage restriction such as PII data of the application service may only be utilized (e.g., processed, stored, used, communicated, transformed, etc.) within a particular geographic location, then the application service may be configured so that PII data, identifiable utilizing the DCaC mechanism, is limited to being utilized in the particular geographic location.

FIG.7illustrates an example simplified procedure (e.g., a method) for generating data compliance constraints, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device200), may perform procedure700by executing stored instructions (e.g., data compliance as code process248).

The procedure700may start at step705, and continues to step710, where, as described in greater detail above, a device may extract, from one or more bodies of text, a data usage restriction for a particular type of data. The one or more bodies of text comprise a law, a regulation, an industry rule, etc.

The data usage restriction may be extracted from the one or more bodies of text by applying a domain-specific data compliance ontology to the one or more bodies of text. For example, the data usage restriction from the one or more bodies of data may be extracted based on an industry associated with the user. For example, an industry-specific ontology may be leveraged in identifying and extracting the data usage restrictions for a particular industry. Alternatively, or additionally, an industry-specific ontology may be leveraged in identifying and extracting the data usage restrictions for a particular geolocation. In addition, data usage restrictions may be extracted from ontology-based online forms or extracted directly from an ontology.

At step715, as detailed above, a device may send, to a user interface, the data usage restriction extracted from the one or more bodies of text for presentation to a user.

At step720, the device may receive, via the user interface, feedback from the user regarding the data usage restriction. The feedback may indicate an additional restriction to the data usage restriction. For example, the feedback may further restrict how the subject data type is handled by the application service and/or its users.

At step725, where, as detailed above, the device may generate a data compliance constraint that controls how an application service handles the particular type of data based on the data usage restriction and the feedback from the user. The data compliance constraint may indicate one or more geolocations in which the particular type of data can be stored. The data compliance constraint may indicate one or more geolocations from which the particular type of data can be accessed. The data compliance constraint may indicate how the particular type of data can be utilized (e.g., processed, stored, used, communicated, transformed, etc.). The data compliance constraint generated by the device may be provided to a workload engine associated with the application service that manages a deployment of the application service to a networked infrastructure according to the data compliance constraint.

The data compliance constraint may be updated in response to detecting a change in the one or more bodies of text. For example, the one or more bodies of text may be continuously or periodically monitored in order to detect changes to the data usage restrictions. When a change is detected, the changed data usage restriction may be extracted and sent to a user interface for presentation to a user, where the user's feedback on the changed data usage restriction may be received and an updated data compliance constraint may be generated based on the changed data usage restriction and/or the user's feedback.

In the absence of these techniques, organizations suffer from poor automation and lack the capacity to programmatically adapt to the different and ever-changing data compliance regulations. Indeed, outside of these techniques, organizations have no means of extracting information programmatically from data usage restrictions and turning such knowledge into actionable data compliance constraints that workload engines can parse, and use in order to instrument the deployment of an application subject to data sovereignty obligations and other rules across different regions. Multi-cloud and edge functionalities pose additional layers of complexity.

The techniques described herein, therefore, may provide these functionalities by leveraging the commonalities, the wide reuse of terms, and the semantic relatedness between the various concepts that are subject to data protection and other data regulations across different regions and industries. Exploiting the solution space for data compliance constraints being discrete, bounded, and sparse, the described techniques introduce programmatic extraction of data usage restrictions from various possible sources, along with automated inference of actionable data compliance constraints for applications subject to data sovereignty and other compliance rules, which may apply across different geographical locations, multi-cloud, and edge.

For example, the techniques described herein provide the programmatic extraction of data usage restrictions from various possible sources, including regulatory documents and ontology-based inputs capturing such restrictions. Further, the techniques provide automated rendering of data usage restriction digests both in human-readable and machine-readable formats representing the data compliance obligations extracted from various expressions of data usage restrictions, including regulatory documents and ontology-based inputs capturing such obligations. The techniques may provide both semi-automated and automated auditing of the computed data usage restrictions. Furthermore, the techniques provide for approval, refinement, and/or re-computation of the extracted data usage restrictions based on the semi-automated and automated auditing results. Moreover, these techniques enable the storage and reuse of human-readable and machine-readable data usage restriction digests capturing data compliance regulations at province, state, national, multi-country, and industry level across several geographical locations. The techniques provide for the use of such data usage restriction digests and the automatic inference of actionable data compliance constraints for applications subject to various data sovereignty and compliance rules, which may apply across different geographical locations, multi-cloud, and edge.

As such, the techniques provide a mechanism, configured to involve varying levels of automation, to determine, dynamically update, and/or implement the set of data compliance constraints that must be applied to an application service in order to satisfy various data handling obligations. The techniques may be utilized by organizations using and/or managing application services in various industries (e.g., healthcare, financial, energy, and other sectors) as well as by SaaS providers. These techniques may enable infrastructure-neutral data compliance controls allowing organizations to reduce the complexity, risks, and cost of dealing with data compliance (from multi-cloud to edge). The techniques may guarantee and future-proof residency/compliancy for applications used by the organizations and may be utilized to resolve and/or grant data compliance dynamically without complex and extensive re-engineering efforts. The techniques may be applied and/or utilized during the testing, staging, and production phases of an application.

While there have been shown and described illustrative embodiments to generate data compliance constraints, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to using the techniques herein for certain purposes, the techniques herein may be applicable to any number of other use cases, as well.