Production Data Scrambling for Accurate, Compliant Test Environments
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Solution Overview
Problem
Entities face challenges in securing and managing confidential and personal data in test environments due to complex data structures and the inability to manually identify all data locations, leading to potential reputational damage, legal ramifications, and compliance violations.
Innovation Solution
A system and method for securing production data by scanning for personal or confidential data, generating a list of attributes, determining a scrambling method for each attribute, and scrambling the data, while providing a compliance report for use in a test environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual production data is used in test environment, then testing accuracy and functionality validation are improved, but data privacy and compliance risks increase
Solution Approach 1:
The system creates scrambled copies of production data that preserve the structural and functional characteristics needed for testing while replacing actual sensitive values with fictitious ones. This allows the test environment to use data copies that maintain testing accuracy without exposing real personal or confidential information.
Solution Approach 2:
The patent introduces an intermediary process (scrambling mechanism) between production data and test environment usage. This intermediary transforms the data by preserving structural relationships while obscuring sensitive values, thereby mediating between the need for accurate testing and the requirement for data privacy protection.
2Object-affected harmful factors
If manual data identification is used, then data security control is improved, but ability to identify all data locations deteriorates due to complex data structures
Solution Approach 1:
The system employs automated scanning mechanisms that independently identify and locate personal and confidential data across complex data structures without requiring manual intervention. The automation self-navigates through redundant storage, indexed data, and complex relationships to comprehensively identify all data locations.
Solution Approach 2:
The patent replaces manual mechanical data identification processes with automated computational scanning systems. This substitution enables the system to efficiently navigate and identify data locations in complex structures that would be infeasible to manually search, thereby maintaining security control while overcoming identification difficulties.
3Object-affected harmful factors
If data scrambling is applied to all attributes, then data privacy protection is improved, but testing functionality may be compromised
Solution Approach 1:
The system applies different scrambling intensities to different attributes based on their sensitivity and testing requirements. Critical personal data receives strong scrambling protection, while attributes essential for functional validation maintain their structural relationships. This localized quality approach ensures privacy protection where needed while preserving testing functionality where appropriate.
Solution Approach 2:
The patent dynamically adjusts scrambling parameters based on attribute characteristics and testing needs. By changing the degree and method of scrambling applied to different data attributes, the system optimizes the balance between privacy protection and functionality preservation, allowing effective testing without compromising security.
Data Source
AI summary
Systems and methods are provided for generating a combined list of attributes for at least one selected object by combining known attributes and a list of attributes for custom tables, determining a scrambling method for each attribute in the combined list of attributes for the at least one selected object, and scrambling each attribute of the combined list of attributes for the at least one selected object, according to the scrambling method for each attribute. The systems and methods further provided for generating a compliance report indicating what was changed in a system by the scrambling of each attribute and what scrambling methods were applied and allowing release of production data comprising the scrambled attributes for the at least one selected object, to a test system for use in testing functionality for an application or service.