Knowledge Graph Data Sovereignty Governance Framework
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Solution Overview
Problem
Organizations face challenges in ensuring compliance with evolving data laws and regulations across multiple geographies, as they struggle to identify and adhere to applicable data sovereignty and localization laws, leading to potential penalties and loss of brand value.
Innovation Solution
A knowledge graph-based data sovereignty governance framework is implemented, which analyzes an application's relational database to classify data based on data classification rules, regional sovereignty laws, and cloud service provider information, prioritizing applicable laws and recommending optimal cloud deployment strategies to ensure data sovereignty compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If organizations manually track and analyze data laws across multiple geographies, then they can identify applicable regulations, but the complexity and time required increases significantly
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure that pre-organizes relationships between data laws, regulations, and cloud service providers. This knowledge graph serves as a mediator between the complex legal framework and the compliance analysis process, enabling automated querying and identification of applicable regulations without manually navigating through multiple geography-specific laws.
Solution Approach 2:
The system performs preliminary actions by pre-processing and structuring data laws and regulations into a knowledge graph before compliance analysis is needed. This includes pre-establishing relationships between regulations, jurisdictions, and cloud service providers, so that when compliance analysis is required, the system can quickly query pre-organized information rather than starting from scratch.
2Reliability
If organizations implement comprehensive data classification across all application data, then they can ensure regulatory compliance, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the data classification process by categorizing application data into different types (e.g., personal data, sensitive data, confidential data) and applying different classification rules to each segment. The knowledge graph enables the system to segment the compliance analysis by identifying only the relevant regulations applicable to each data type and jurisdiction, rather than analyzing all possible regulations against all data.
Solution Approach 2:
The system applies local quality by tailoring the classification approach to specific contexts - different classification rules and regulatory requirements are applied based on the data type, jurisdiction, and cloud service provider being analyzed. The knowledge graph enables the system to retrieve context-specific regulatory requirements rather than applying uniform classification across all scenarios.
3Measurement precision
If organizations analyze relational databases to classify data according to multiple sovereignty laws, then they can identify applicable regulations, but the analytical complexity and resource requirements worsen
Solution Approach 1:
The knowledge graph serves as an intermediary that pre-structures the complex relationships between data characteristics, jurisdictions, and regulations. Instead of directly analyzing relational databases against multiple sovereignty laws, the system uses the knowledge graph to mediate the analysis by pre-organizing which regulations apply to which data types in which jurisdictions, thereby simplifying the detection process.
Solution Approach 2:
The system performs preliminary action by pre-processing the regulatory framework into a knowledge graph that encodes relationships between data types, jurisdictions, and applicable laws. This pre-processing includes establishing rules for determining regulation applicability based on data location, type, and sensitivity, so that the actual compliance analysis can query pre-computed relationships rather than performing complex real-time analysis.
4Adaptability or versatility
If organizations prioritize applicable laws and recommend cloud deployment strategies, then they can ensure data sovereignty compliance, but the system complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces dynamics by making the governance framework adaptable to different scenarios through the knowledge graph. The system can dynamically retrieve and apply different regulatory requirements, data classification rules, and cloud service provider information based on the specific context of each compliance analysis request. This enables flexible deployment strategy recommendations that adapt to varying data sovereignty requirements across different jurisdictions and data types.
Data Source
AI summary
In some examples, data classification and modelling based application compliance analysis may include generating, for application data for an application, and based on data laws, a knowledge graph schema that is used to generate an instantiated knowledge graph. Based on a plurality of data sources, domain knowledge and patterns, and the instantiated knowledge graph, classified data may be generated to generate an annotated knowledge graph. Based on the annotated knowledge graph, an indication of applicable sovereign laws and compliance related to the application data for the application may be generated. Based on the annotated knowledge graph and the applicable sovereign laws and compliance related to the application data for the application, a sovereign cloud strategy related to the application data for the application may be generated. Further, the sovereign cloud strategy may be applied to the application data for the application.