Data Sanitization System for Secure Application Development
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Solution Overview
Problem
Existing systems lack the capability to sanitize and validate sensitive information, such as Non-Public Information, confidential data, and private data, at an element level before moving it from production environments to testing environments, leading to potential data security breaches.
Innovation Solution
A system that establishes communication links with disparate systems, sanitizes data by obfuscating sensitive information, generates and executes queries based on rules to validate sanitized data, and transmits validated data to non-production environments, while generating alerts and implementing remediation steps as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data is extracted from production environments for testing, then real-life data quality is improved, but data security is worsened due to sensitive information exposure
Solution Approach 1:
The patent segments data into different categories (sensitive vs. non-sensitive) and applies different processing treatments to each segment. Sensitive data elements are identified and sanitized through obfuscation techniques, while non-sensitive data is transferred as-is, thus maintaining data quality for testing while protecting sensitive information.
Solution Approach 2:
The patent introduces an intermediary data sanitization layer between the production environment and testing environment. This intermediary process automatically detects, masks, or transforms sensitive data elements before data leaves the production system, allowing real data to be used for testing without exposing sensitive information.
2Object-affected harmful factors
If data sanitization is performed manually, then data security is improved, but processing time and labor costs increase
Solution Approach 1:
The patent implements self-service automated data sanitization capabilities that operate without manual intervention. The system automatically scans data for sensitive elements, applies appropriate sanitization rules, and validates the sanitization process, dramatically reducing both time and labor costs while maintaining security standards.
Solution Approach 2:
The patent replaces manual mechanical sanitization processes with automated computational systems. Machine learning algorithms and automated rule engines substitute for human reviewers, enabling rapid processing of large datasets while maintaining consistent security standards without human labor.
3Object-affected harmful factors
If comprehensive data validation is implemented, then data security is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal validation framework that handles multiple data types, sensitivity levels, and sanitization methods through a single integrated system. This multi-functional approach consolidates what would otherwise require multiple separate validation processes, reducing overall system complexity while maintaining comprehensive security validation.
Data Source
AI summary
Embodiments of the present invention provide a system for provisioning validated sanitized data for application development. The system is configured for establishing a communication link with a plurality of disparate systems, retrieving data from the plurality of disparate systems via the communication link, sanitizing the data retrieved from the plurality of disparate systems, generating a query to validate the sanitized data, wherein the generation of the query is based on a set of rules, validating the sanitized data using the query generated based on the set of rules, determining that the validation of the sanitized data is successful, and transmitting the validated sanitized data to a second plurality of disparate systems.