Cross-Border Data Visualization for Privacy Compliance
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Solution Overview
Problem
Current systems lack effective methods to manage and comply with diverse personal data handling requirements across different jurisdictions, leading to increased risks of data breaches and unauthorized access, and individuals face challenges in minimizing data processing by entities they do not actively engage with.
Innovation Solution
A data model generation and population system that determines the type of operation and geographic location of computing systems, generates data structures for control actions, and provides questionnaires to users to assess compliance, comparing answers to identify relative readiness and suggesting improvements based on best practices from similarly situated entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If comprehensive data collection and processing is implemented to ensure compliance with diverse jurisdictional requirements, then compliance coverage is improved, but system complexity and data security risks increase
Solution Approach 1:
The system segments compliance requirements by jurisdiction and data type, organizing diverse regulatory obligations into structured categories. This allows the system to manage complexity through modular organization while maintaining comprehensive coverage across multiple jurisdictions.
Solution Approach 2:
The patent introduces intermediary components including data brokers and verification services that mediate between entities and regulatory requirements. These intermediaries simplify compliance by handling data verification and validation, reducing the direct complexity burden on the core system.
2Measurement precision
If extensive data processing by multiple entities is performed to enable comprehensive compliance assessment, then assessment accuracy is improved, but data security risks and unauthorized access potential increase
Solution Approach 1:
The system performs preliminary verification and validation of data before it enters the main processing pipeline. By pre- validating data authenticity and compliance status, the system reduces security risks while maintaining assessment accuracy through controlled data flow.
Solution Approach 2:
The patent implements feedback mechanisms where verification results and compliance assessments are continuously monitored and fed back into the system. This allows real-time detection of security anomalies and adjustment of processing parameters to maintain both accuracy and security.
3Measurement precision
If manual compliance verification processes are used to ensure thorough assessment, then verification accuracy is improved, but processing time and operational efficiency decrease
Solution Approach 1:
The system enables self-service compliance verification where entities can automatically submit and verify their own compliance status through standardized interfaces. This maintains verification accuracy through structured validation while dramatically improving processing efficiency by eliminating manual intervention for routine assessments.
Solution Approach 2:
The patent replaces manual verification processes with automated computational systems that use algorithms and data analysis to perform compliance assessments. This substitution maintains or improves verification accuracy while significantly increasing processing speed and efficiency.
4Device complexity
If individuals have limited control over which entities process their data, then system simplicity is maintained, but user privacy protection and data security are worsened
Solution Approach 1:
The patent introduces a new dimension of user control through preference settings and consent management interfaces. Users can specify which entities may process their data and under what conditions, adding a layer of control without fundamentally complicating the underlying processing system.
Data Source
AI summary
In particular embodiments, a Cross-Border Visualization Generation System is configured to: (1) identify one or more data assets associated with a particular entity; (2) analyze the one or more data assets to identify one or more data elements stored in the identified one or more data assets; (3) define a plurality of physical locations and identify, for each of the identified one or more data assets, a respective particular physical location of the plurality of physical locations; (4) analyze the identified one or more data elements to determine one or more data transfers between the one or more data systems in different particular physical locations; (5) determine one or more regulations that relate to the one or more data transfers; and (6) generate a visual representation of the one or more data transfers based at least in part on the one or more regulations.