Data Retention Rule Generator for Compliance Management
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Solution Overview
Problem
The increasing complexity and cost of managing electronic data in IT systems, particularly in retaining information for compliance with legal and business requirements, pose challenges in determining appropriate retention periods and managing data lifecycle effectively.
Innovation Solution
A data retention rule generator system that maps organizational attributes, purpose of data, and legal entities to determine applicable retention rules, integrating these factors into the information lifecycle management process to identify time frames for data entities, and allowing for modification based on secondary purposes and legal retention periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is retained for longer periods to comply with legal and business requirements, then compliance reliability is improved, but storage costs and management complexity increase
Solution Approach 1:
The patent segments data into different categories based on retention requirements (primary purpose data, secondary purpose data, legally required data) and applies different retention rules to each segment. This allows the system to manage complex retention requirements by breaking them down into manageable groups with distinct policies.
Solution Approach 2:
The patent implements dynamic retention periods that can be adjusted based on various factors. The system determines retention rules that may change over time or based on conditions, allowing flexibility in managing data retention while maintaining compliance with legal and business requirements.
2Reliability
If data retention periods are extended to meet legal requirements, then legal compliance is improved, but storage costs increase
Solution Approach 1:
The patent extracts only the data that legally requires extended retention from the overall data set. By identifying and separating legally required data from other data, the system maintains necessary compliance while avoiding unnecessary storage costs for data that does not require long-term retention.
Solution Approach 2:
The patent changes the retention parameter (time period) based on the specific category of data and applicable legal requirements. Different data categories have different retention periods, allowing the system to optimize storage costs by not uniformly retaining all data for the maximum possible period.
3Measurement precision
If multiple retention rules are applied to different data categories, then compliance accuracy is improved, but rule determination complexity increases
Solution Approach 1:
The patent performs preliminary classification of data into categories with specific retention requirements before applying retention rules. By pre-organizing data based on its retention needs, the system simplifies the subsequent application of retention rules and reduces the complexity of determining which rules apply to which data.
Solution Approach 2:
The patent introduces an intermediary classification layer that maps data to appropriate retention rules. This intermediary step categorizes data based on characteristics such as primary/secondary purpose and legal requirements, making the rule determination process more systematic and less complex.
Data Source
AI summary
Various embodiments of systems and methods to determine data retention rules for data entities are described herein. In one aspect, the data entities are obtained. Usage statuses of the data entities are determined. One or more purpose of data corresponding to the one or more data entities is received. Further, legal entities corresponding to the one or more data entities are identified based on line organization attributes and the usage statuses. Process object attributes associated with the one or more data entities are identified based on the legal entities. Retention rules for the one or more data entities are determined based on the one or more purpose of data, the legal entities and the process object attributes.