Policy-Based Cloud Architecture Generation for Data Residency Compliance
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Solution Overview
Problem
Existing software applications face challenges in complying with diverse geographic data residency and localization policies, leading to non-compliance issues and potential penalties, as they struggle to determine optimal deployment architectures that adhere to varying regulations across different locations.
Innovation Solution
A policy-based application architecture generation framework that uses a knowledge model to analyze data residency laws, vendor characteristics, and multi-criteria decision techniques to determine compliant cloud architectures, enabling automated identification of policy-compliant deployment strategies and remediation of non-compliance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing software applications are deployed across multiple geographies to serve dispersed users, then user accessibility and service coverage are improved, but compliance with diverse geographic data residency and localization policies becomes more difficult to achieve
Solution Approach 1:
The patent implements local quality by deploying application components and data storage in specific geographic locations tailored to local data residency requirements. The system identifies compliant cloud service provider locations for each geographic region and configures the application architecture to store and process data locally according to regional policies, thereby maintaining both user accessibility and policy compliance simultaneously
2Adaptability or versatility
If cloud-based deployment is used to enable flexible and scalable architecture, then deployment flexibility and scalability are improved, but determining optimal compliant deployment architectures becomes more complex
Solution Approach 1:
The patent implements feedback by incorporating automated compliance assessment mechanisms that continuously evaluate the deployment architecture against applicable data residency policies. The system receives feedback from policy evaluation results and automatically adjusts deployment configurations, cloud service provider selections, and data storage locations to maintain compliance while optimizing architecture, thereby reducing the complexity of manual determination
3Reliability
If manual architecture design is used to ensure policy compliance, then compliance accuracy is improved, but deployment time and operational efficiency are reduced
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine compliant deployment architectures without requiring manual intervention. The architecture generation system autonomously analyzes applicable policies, evaluates cloud service provider options, selects optimal locations, and configures deployment parameters, thereby achieving both high compliance accuracy and rapid deployment efficiency simultaneously
Data Source
AI summary
In some examples, policy-based application architecture generation may include generating, based on a knowledge model schema, data residency policies, and a data classification ontology, a knowledge model, and determining, based on the knowledge model, whether an application includes regulated data. Based on an analysis of application data and user data, user location and regulated data insights may be generated to determine location specific data residency policies. Location specific regulated data in-flow and data source hosted location insights may be analyzed to determine a location compliance assessment that includes an indication of whether a location associated with the application is compliant or not compliant with the location specific data residency policies. Based on an indication of non-compliance, a strategy may be generated for compliance of application architecture for the application with the location specific data residency policies, and re-architecting of the application architecture may be implemented.