Centralized Data Retention System for Multi-Source Deletion
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Solution Overview
Problem
Existing data storage and processing systems face challenges in efficiently handling data deletion requests, particularly in identifying and deleting data stored across multiple sources while ensuring compliance with regulations like GDPR and preventing improper deletion.
Innovation Solution
A centralized data retention and deletion system that ingests data from multiple external sources, performs eligibility checks, and applies modification logic to identify and delete relevant data, while providing user interfaces for oversight and validation to prevent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored across multiple external data sources, then data availability and accessibility are improved, but data deletion complexity and risk increase
Solution Approach 1:
The patent introduces a centralized data retention and deletion system that acts as an intermediary between data subjects and multiple external data sources. This central system receives deletion requests, identifies relevant data across distributed sources using search terms and metadata, coordinates the deletion process, and provides oversight through user interfaces. This intermediary approach maintains data accessibility across multiple sources while centralizing the complex deletion coordination function.
Solution Approach 2:
The system segments the data deletion process into distinct functional modules: request reception, eligibility checking, search term generation, data identification across sources, deletion execution, and oversight provision. This segmentation allows each component to handle specific aspects of the complex deletion task independently, making the overall process more manageable and less error-prone.
2Measurement precision
If manual review of data for deletion is performed, then deletion accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by generating search terms from data subject identifiers before the actual deletion process. It pre-identifies potential data locations across multiple external sources using these search terms and metadata. This preliminary identification and validation step ensures accuracy is established before deletion execution, reducing the need for post-deletion corrections while maintaining efficient processing.
Solution Approach 2:
The system implements feedback mechanisms where deletion requests are reviewed against eligibility criteria, and the identified data is validated before deletion execution. The centralized system provides feedback loops that allow verification of data identification accuracy and enable correction of identification errors before actual deletion occurs, maintaining high accuracy without requiring complete manual review.
3Productivity
If automated deletion processes are implemented, then processing efficiency is improved, but risk of improper deletion increases
Solution Approach 1:
The system performs preliminary eligibility checks and data identification validations before automated deletion execution. It generates search terms, identifies data across sources, and validates the identified data against the deletion request criteria before proceeding with automated deletion. This preliminary validation layer ensures automated processes operate on correctly identified data, reducing the risk of improper deletion while maintaining processing efficiency.
Solution Approach 2:
The centralized data retention and deletion system serves as an intermediary that orchestrates automated deletion processes across multiple external sources. It implements automated eligibility checking, data identification, and deletion coordination while providing oversight mechanisms. This intermediary automated system reduces manual intervention needs while maintaining reliability through centralized control and validation protocols.
4Measurement precision
If comprehensive data identification across multiple sources is performed, then deletion completeness is improved, but system complexity and computational resources increase
Solution Approach 1:
The system creates and uses search terms as simplified representations or copies of data subject identifiers to locate actual data across external sources. Instead of directly querying complex data structures at multiple sources, it uses generated search terms and metadata to identify relevant data locations. This copying approach simplifies the identification process while maintaining comprehensive coverage across distributed data sources.
Solution Approach 2:
The centralized system acts as an intermediary that manages the complexity of cross-source data identification. It handles search term generation, coordinates queries across multiple external sources, aggregates results, and validates completeness. This intermediary layer abstracts the complexity of comprehensive multi-source identification from individual data sources while ensuring thorough data location and deletion.
Data Source
AI summary
Disclosed herein are systems and techniques for centralized data retention and deletion. Data can be ingested from multiple external data sources and saved internally for use to process data modification (e.g., deletion) requests via a data processing pipeline, which may apply eligibility checks and modification logic to determine the appropriate modifications to the relevant data items to comply with the data modification request. Various user interfaces may be generated to provide a user with oversight of the data processing pipeline and the data modifications. The user may review and trigger the modification of data stored at the external data sources and/or internally.