System architecture for designing and monitoring privacy-aware services
The present disclosure provides a system architecture for designing and monitoring privacy-aware services and improving privacy regulation compliance. A privacy-preserving knowledge graph (PPKG) system provides functionality for modelling and analyzing processes that use, share, or request sensitive data from users and the outcomes of such functionality may be utilized to modify the design of the processes (e.g., to improve security of the process, regulatory compliance of the process, and the like). The PPKG system may also be used to modify the process, such as to write code that may be compiled into executable form and deployed to a run-time environment. A privacy-preserving posture (PPP) system monitors the run-time environment and analyzes where processes obtain, store, and share sensitive data. The PPP system may identify run-time vulnerabilities that may pose risks with respect to the sensitive data, as well as areas where modifications could be made to improve regulatory compliance.
1. A method comprising:
analyzing, by one or more processors, information associated with one or more privacy regulations of a process of at least one of an enterprise system and a supply chain system,
wherein the process is configured to utilize sensitive data of a user;
evaluating, by the one or more processors, compliance of the process with the one or more privacy regulations based, at least in part, on a model of the process,
wherein the model is generated based on information obtained via the analyzing;
producing recommendation to modify the process to improve regulatory compliance associated with the one or more privacy regulations based on the evaluating;
determining, by the one or more processors, one or more modifications to the process based on the produced recommendation;
generating updated executable code for the process based on at least one modification of the one or more modifications; and
deploying, by the one or more processors, the updated executable code to a run-time environment, wherein the executable code is executed by the one or more processors to implement the process within the runtime environment.
2. The method of claim 1 , further comprising generating one or more data-in-motion rules configured to at least partially anonymize the sensitive data of the user.
3. The method of claim 1 , wherein the one or more modifications comprises applying at least one data-in-motion rule of the one or more data-in-motion rules to the process.
4. The method of claim 1 , further comprising:
training an artificial intelligence algorithm based on a set of anonymized data, wherein the analyzing is based on the trained artificial intelligence algorithm, and wherein the artificial intelligence algorithm is configured to determine, at least in part, the modification to the process.
5. The method of claim 4 , wherein the training is performed based on data stored at a data lake.
6. The method of claim 1 , wherein the analyzing comprises simulating the process using anonymized user data.
7. The method of claim 1 , wherein the process comprises sharing the sensitive data provided to the enterprise system by the user with the supply chain system, sharing the sensitive data provided to the enterprise system by the user with another process of the enterprise system or obtaining the sensitive data from the user.
8. The method of claim 1 , wherein the analyzing comprises determining whether the process utilizes the sensitive data of the user in accordance with a consent of the user.
9. The method of claim 8 , further comprising obtaining an updated consent of the user when the process utilizes the sensitive data of the user in a manner that is not in accordance with the consent of the user.
10. The method of claim 1 , further comprising:
receiving a withdrawal of consent from the user; and
providing information identifying an impact of the withdrawal of consent to a user device.
11. The method of claim 1 , further comprising generating a plurality of ontologies, the plurality of ontologies comprising ontologies associated with privacy, legal, risk, and domain relations, wherein the analyzing is based on at least one ontology of the plurality of ontologies.
12. A method comprising:
monitoring, by one or more processors, a process of an enterprise system;
detecting, by the one or more processors, sensitive data provided to the process from a user device based on the monitoring;
generating, by the one or more processors, a canonical knowledge graph representative of the sensitive data provided to the process from the user devices based on the monitoring;
generating, by the one or more processors, an attack surface knowledge graph representative of attack pathways and vectors associated with the process based, at least in part, on the monitoring, wherein the attack pathways and vectors correspond to vulnerabilities through which a malicious actor may obtain access to the sensitive data;
generating, by the one or more processors, a broker knowledge graph representative of the sensitive data shared from the process to a supply chain system based on the monitoring, wherein the broker knowledge graph comprises information that identifies a location of the sensitive data shared with the supply chain system and an identifier associated with the process;
performing, by the one or more processors, penetration testing configured to evaluate threats targeting the sensitive data based, at least in part, on the attack surface knowledge graph; and
outputting, by the one or more processors, a recommendation for mitigating threats targeting the sensitive data and identified based on the penetration testing, wherein the recommendation includes at least one modification to executable code for the process wherein the executable code is executable by the one or more processors to implement the process.
13. The method of claim 12 , further comprising applying a data-in-motion rule to the sensitive data shared from the process to the supply chain system, wherein the data-in-motion rule is configured to obfuscate the sensitive data shared with the supply chain system.
14. The method of claim 13 , further comprising selecting the data-in-motion rule based on a type of the sensitive data and a data requirement of the supply chain system.
15. The method of claim 12 , wherein the canonical knowledge graph comprises information that identifies a location of the sensitive data shared with the enterprise system and an identifier associated with the process.
16. A system comprising:
a memory; and
one or more processors communicatively coupled the memory;
a run-time environment executable by the one or more processors; and
wherein the memory stores instructions which, when executed by the one or more processors, cause the one or more processors to:
analyze a first process of at least one of an enterprise system and a supply chain system, wherein the process is configured to utilize first sensitive data of a user;
generate one or more data-in-motion rules configured to at least partially anonymize the first sensitive data of the user;
evaluate compliance of the first process with one or more privacy regulations based, at least in part, on a model of the first process, wherein the model is generated based on information obtained via the analyzing;
determine one or more modifications to the first process based on the evaluating;
generate executable code for the process, wherein the one or more modifications comprises applying at least one data-in-motion rule of the one or more data-in-motion rules to the first process;
generate updated executable code for the first process based on at least one modification of the one or more modifications;
deploy the updated executable code to a run-time environment wherein the executable code is executed by the one or more processors to implement the process within the runtime environment; and
wherein the run-time environment is configured to:
monitor a second process;
detect second sensitive data provided to the process from a user device based on the monitoring;
generate a canonical knowledge graph representative of the second sensitive data provided to the process from the user device based on the monitoring;
generate an attack surface knowledge graph representative of attack pathways and vectors associated with the second process based, at least in part, on the monitoring, wherein the attack pathways and vectors correspond to vulnerabilities through which a malicious actor may obtain access to the second sensitive data;
perform penetration testing configured to evaluate threats targeting the second sensitive data based, at least in part, on the attack surface knowledge graph; and
output a recommendation for mitigating threats targeting the second sensitive data, wherein the threats are identified based on the penetration testing.
17. The system of claim 16 , wherein the run-time environment is configured to:
receive a withdrawal of consent from the user; provide information identifying an impact of the withdrawal of consent to the user device; and trigger deletion of at least one of the first sensitive data and the second sensitive data corresponding to the withdrawal of consent from an external system associated with at least one of the first process and the second process.