Automated Data Retention Rule Reconciliation
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Solution Overview
Problem
Existing data retention frameworks face inefficiencies and compliance challenges due to the complexity of implementing new regulations, leading to potential premature deletion or excessive retention of data, as organizations struggle to automatically reconcile and implement new retention rules with existing ones.
Innovation Solution
A data retention framework is implemented with a hierarchical organization of rules, policies, and areas, utilizing fields such as minimum and maximum retention periods, time factors, and access control lists to automate compliance with data retention regulations, ensuring data is retained or deleted as required by various laws and policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and reconciliation of data retention rules is performed, then compliance accuracy may be maintained, but time consumption and operational efficiency deteriorate significantly
Solution Approach 1:
The system performs automatic self-reconciliation of data retention rules by comparing new rules against existing rules and data, identifying conflicts and resolutions without human intervention. The framework autonomously determines retention periods, access restrictions, and deletion timelines by processing regulations through hierarchical rule structures.
Solution Approach 2:
The patent replaces manual mechanical processes of rule identification, comparison, and reconciliation with an automated computational system. The framework uses computer-implemented methods to parse, compare, and resolve retention rule conflicts, substituting human operational mechanics with algorithmic processing.
2Reliability
If comprehensive manual review of all customer data is conducted to identify retention requirements, then compliance coverage is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The data retention framework serves multiple functions simultaneously: it identifies applicable retention rules, determines retention periods, restricts access, schedules deletions, and reconciles conflicts across different regulations. This multi-functional system replaces multiple separate manual processes with a single automated framework.
Solution Approach 2:
The system automatically performs comprehensive compliance coverage by scanning all stored data, identifying applicable retention rules based on data characteristics, and enforcing appropriate retention policies without requiring manual review of each data element.
3Reliability
If frequent rechecking of data retention status is performed to accommodate new legislation, then compliance accuracy is maintained, but operational complexity and time loss increase
Solution Approach 1:
The framework establishes continuous feedback loops that automatically monitor for new retention regulations and re-evaluate existing data against updated rules. The system provides ongoing compliance verification without requiring manual rechecking, using automated feedback mechanisms to adjust retention policies when new legislation is detected.
Solution Approach 2:
The retention framework is designed to be dynamic and adaptable, automatically adjusting to new legislation without fixed operational schedules. The system continuously evaluates the applicability of retention rules and modifies data retention status based on evolving regulatory requirements, rather than following static rechecking cycles.
4Productivity
If automated reconciliation systems are implemented, then productivity and efficiency are improved, but system complexity increases
Solution Approach 1:
The automated reconciliation system is segmented into distinct functional modules: rule identification, retention period determination, access restriction enforcement, deletion scheduling, and conflict resolution. This modular architecture manages system complexity by dividing the automated framework into manageable, independent components that can be processed separately.
Solution Approach 2:
The patent introduces a hierarchical dimension to the retention rule structure, organizing rules by jurisdiction, data type, and retention category. This dimensional organization allows the automated system to navigate complex regulatory landscapes systematically, reducing operational complexity through structured rule hierarchies.
Data Source
AI summary
Data retention requirement rules may be created to have an area association, a minimum retention period specification, and a maximum retention period specification associated with one or more rules. Users may be assigned to one or more areas of at least one rule. Data objects may associated with one or more areas that may be associated with a rule. As the rules are updated and/or changed, the minimum and maximum periods in different rules may be compared in real time to implement a data retention policy that automatically deletes, preserves, and/or prevents access to data objects according to the each of the rules.