Location-Based Copy Data Management via Rules Engine
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Solution Overview
Problem
Existing systems lack an efficient method for managing location-based copy data, particularly in scenarios where user data must be formatted according to location-dependent rules and updated rules, ensuring compliance across multiple jurisdictions while preventing the need to modify application code.
Innovation Solution
A system utilizing a rules engine, index engine, and action engine to process and manage copy data by detecting data types, applying relevant rules, continuously indexing metadata, and executing actions such as redaction, encryption, or redirection, with passive feedback loops for automatic updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user data is stored across multiple data stores in multiple different locations, then data availability and accessibility are improved, but data management complexity and compliance difficulty increase
Solution Approach 1:
The patent introduces an intermediary system comprising an index engine, rules engine, and action engine that mediates between multiple data stores and application code. This intermediary layer handles location-based rule application, data transformation, and compliance enforcement centrally, eliminating the need for each application to individually manage complex multi-location data storage rules.
Solution Approach 2:
The system creates a universal data management layer that serves multiple data stores simultaneously with a single set of rules. The rules engine applies location-dependent rules across all data stores uniformly, and the action engine executes appropriate actions (redact, encrypt, redirect) based on the target location's requirements, providing multi-functional compliance management.
2Reliability
If application code is modified to comply with new location-dependent rules, then compliance accuracy is improved, but development time and system maintenance effort increase
Solution Approach 1:
The system implements dynamic rule management where location-dependent rules can be updated without modifying application code. The rules engine continuously monitors for rule changes and automatically applies updated rules to data operations, allowing compliance requirements to adapt dynamically while keeping the application codebase static.
Solution Approach 2:
The system performs preliminary actions by pre-processing data through the rules engine before data is stored or accessed. The action engine executes compliance actions (redaction, encryption, redirection) in advance based on predicted data operations, ensuring compliance is built-in before data reaches its destination rather than requiring post-hoc code modifications.
3Reliability
If data is processed and transformed according to location-dependent rules, then data security and privacy are improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial actions by selectively transforming only the portions of data that require compliance treatment based on location-specific rules. The action engine determines the minimum necessary actions (redact specific fields, encrypt only sensitive data, redirect to appropriate locations) rather than processing entire datasets uniformly, reducing unnecessary computational overhead.
Solution Approach 2:
The rules engine performs preliminary classification and routing of data before full processing occurs. By pre-determining which data requires transformation and what actions are needed based on location metadata, the system avoids unnecessary processing of compliant data, thereby maintaining processing efficiency while ensuring security requirements are met.
Data Source
AI summary
The present disclosure is directed to a novel system for active and passive management of location-based copy data. The system may intake user data from various data sources into a rules engine, which contains the decision logic to format the incoming data according to location-dependent rules. The system may continuously index the incoming data as well as metadata (e.g., data source, data storage location, rules associated with the data, or the like). Based on the output of the rules engine and/or index engine, an action engine may execute various processing steps to condition the data for storage in a particular system. Furthermore, the system may use passive and continuous rule updates with the process as described herein to automatically update the stored user data to conform to the new rules.