Data Privacy Integration for Multi-Application Correction Requests
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Solution Overview
Problem
Existing data management systems face inefficiencies and resource wastage in handling data subject requests for data correction and restriction across multiple applications, particularly due to the complexity of locating and correcting or restricting data dispersed across disparate systems.
Innovation Solution
A centralized data privacy integration service that uses machine learning and responder groups to efficiently identify and manage data correction and restriction requests across multiple applications, reducing resource consumption and ensuring consistent compliance with data protection regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized data privacy integration service is implemented to manage data correction and restriction requests across multiple applications, then processing efficiency and resource utilization are improved, but system complexity increases
Solution Approach 1:
The patent introduces a centralized data privacy integration service as an intermediary component that mediates between data subject requests and multiple disparate applications. This service receives correction and restriction requests, identifies relevant applications, coordinates the changes across systems, and manages the workflow centrally. By positioning this integration service as a mediator, the system achieves improved processing efficiency without requiring direct complex interconnections between all applications, thus resolving the contradiction between productivity improvement and system complexity increase.
2Extent of automation
If automated machine learning models are used to evaluate and determine data correction requests, then processing speed and automation level are improved, but accuracy and reliability may be compromised
Solution Approach 1:
The patent segments the automated decision-making process into distinct evaluation stages handled by the machine learning model. The model evaluates specific features of data correction requests (such as request validity, data subject consent, and applicable regulations) and provides structured recommendations. This segmentation allows the system to achieve high automation levels for routine evaluations while maintaining the ability to review and override decisions, thus balancing automation efficiency with decision reliability.
3Loss of information
If comprehensive data retrieval protocols are initiated across all applications to identify relevant data, then data completeness is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent implements preliminary action by maintaining metadata registries and data catalogs across applications that pre-record information about data storage locations, types, and relationships. When a data correction or restriction request is received, the centralized service queries these pre-established registries to quickly identify relevant applications and data, rather than conducting comprehensive searches across all systems. This preliminary organization of data information enables the system to achieve data completeness without triggering resource-intensive full-system scans, thus resolving the contradiction between data completeness and resource consumption.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for data privacy. One example method includes receiving a first request to correct personal data or restrict processing of personal data of a data subject. Response data is identified that is provided by applications in a multiple-application landscape in response to a second request for access to personal data processed by respective applications in a multiple-application landscape. Relevant applications for the first request are identified based on the response data. A data correction or data restriction work package is sent to each relevant application and data correction or data restriction work package responses are received from relevant applications. An overall data correction or data restriction result is determined based on the data correction or data restriction work package responses and is provided in response to the first request.