Erasure Component for Selective Data Retention
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Solution Overview
Problem
Existing systems are inadequate in managing the erasure and retention of user data stored in data stores, particularly in ensuring data security and privacy compliance with laws like GDPR and CCPA, as they lack robustness and efficiency in handling and usage of data.
Innovation Solution
A data lifecycle discovery platform (DLDP) with an erasure component that uses artificial intelligence and machine learning to classify data, determine erasure eligibility scores, and manage data retention and erasure based on legal and contractual obligations, ensuring compliance with data protection regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is retained in data stores for business operations, then productivity and service capability are improved, but data security and privacy compliance become more difficult to maintain
Solution Approach 1:
The patent segments data into different categories (personal data, sensitive personal data, non-personal data) and applies different retention and erasure rules to each segment. The system automatically identifies and classifies data types, then selectively erases only the portions required by compliance obligations while retaining other data needed for business operations, thus maintaining productivity while ensuring compliance.
Solution Approach 2:
The patent introduces an intermediary erasure component that acts as a mediator between data storage systems and compliance requirements. This component intercepts erasure requests, analyzes them against retention policies, and executes selective erasure operations. It also introduces an erasure eligibility scoring mechanism that evaluates whether data should be erased based on multiple factors including compliance obligations and business needs, serving as a buffer that reconciles the conflict between retention and erasure.
2Reliability
If manual data erasure processes are used, then data security can be maintained, but the complexity and time required to process erasure requests increase significantly
Solution Approach 1:
The patent implements self-service capabilities where the system automatically processes erasure requests without requiring manual intervention. The erasure component automatically receives erasure requests, classifies the data involved, determines erasure eligibility using scoring mechanisms, executes the erasure operations across multiple data stores, and generates compliance reports. This automation maintains security through consistent policy application while dramatically reducing operational complexity and processing time.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of manual review and execution of erasure requests, the system uses automated data classification algorithms, erasure eligibility scoring models, and programmatic erasure execution. This substitution reduces human error, ensures consistent application of compliance rules, and significantly decreases the time and complexity associated with processing erasure requests.
3Reliability
If comprehensive data erasure is performed to ensure privacy compliance, then data protection is improved, but loss of valuable user data for future business operations occurs
Solution Approach 1:
The patent applies local quality by treating different portions of data differently based on their specific characteristics and compliance requirements. Rather than applying a blanket erasure policy, the system analyzes each data element's type, sensitivity, and legal status, then applies erasure only to specific portions that require protection. This allows the organization to maintain valuable non-compliant data (such as aggregated analytics or anonymized information) while erasing only the specific personal data elements that trigger compliance obligations.
Solution Approach 2:
The patent implements partial action by performing selective erasure rather than comprehensive data deletion. The erasure component evaluates erasure requests and executes partial erasure operations that remove only the specific data elements required by compliance obligations while preserving other data that may have continued business value. This approach ensures adequate data protection for compliant elements while retaining useful information for future operations.
4Productivity
If multiple data stores are used to distribute user data globally, then service capability and data accessibility are improved, but the difficulty of managing consistent erasure across all stores increases
Solution Approach 1:
The patent implements a universal erasure component that can operate across multiple diverse data store systems. This component is designed to work with various data store types and locations through standardized interfaces, allowing it to execute erasure operations consistently across the entire distributed data infrastructure. The system maintains a centralized view of data locations and erasure policies, enabling coordinated erasure across multiple stores without requiring separate manual processes for each location.
Data Source
AI summary
Techniques for managing erasure of data are presented. In response to receiving a request for erasure of data from a set of data stores, an erasure component can analyze a set of rules and information relating to the user account, including an account status and erasure hold status associated with the user account. The set of rules can be based on legal or contractual obligations applicable to the set of data stores, and can indicate various conditions under which data associated with a user account of a user can be eligible to be erased from the set of data stores or an associated data vault repository. The erasure component can determine eligibility for erasure of all or a portion of the set of data from the set of data stores based on the analysis results. Erasure component can determine erasure eligibility scores to pre-qualify user accounts for erasure eligibility.