Data Inventory System for Personal Information Scanning and Compliance
Solution Overview
Problem
There is a growing need for improved systems and methods to manage personal data in compliance with privacy and security policies, as frequent breaches of security lead to unauthorized access and the need for better data management practices.
Innovation Solution
A computer-implemented data processing method and system that scans data repositories to identify personal information, uses machine learning techniques to categorize data elements, and generates a data inventory by linking primary and transfer data assets, ensuring compliance with legal and industry standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data management practices are used to comply with privacy policies, then data security can be maintained through human oversight, but productivity and efficiency deteriorate due to time-consuming manual processes
Solution Approach 1:
The system enables automatic self-service through machine learning models that autonomously classify data elements, generate data inventories, and map data flows without requiring manual human intervention for each data item, thereby maintaining security through automated compliance checking while dramatically improving productivity
Solution Approach 2:
The patent replaces manual mechanical data management processes with automated computational systems that use machine learning algorithms to scan, classify, and inventory data elements, substituting human labor with intelligent automated systems that maintain or enhance security while improving efficiency
2Reliability
If comprehensive data scanning and inventory generation are performed to identify all personal data, then data compliance improves, but device complexity and processing time worsen
Solution Approach 1:
The system segments the data inventory process into distinct automated stages: data element identification, classification using machine learning models, inventory generation, and flow mapping. This segmentation allows complex compliance tasks to be handled by specialized automated components, reducing overall system complexity while maintaining comprehensive compliance
Solution Approach 2:
The system performs preliminary automated classification and tagging of data elements as they are discovered during scanning, preparing them in advance for inventory generation and compliance verification. This preliminary action reduces the complexity of subsequent processing steps by having data ready in a structured format
3Productivity
If machine learning techniques are used to automatically categorize data elements, then productivity improves through automation, but measurement precision may worsen due to potential classification errors
Solution Approach 1:
The system incorporates feedback mechanisms where classification results are continuously evaluated and used to refine the machine learning models. Automated verification processes check the accuracy of data element classifications and provide feedback for model improvement, maintaining high precision while preserving the productivity benefits of automation
Solution Approach 2:
The classification system is designed to be dynamic and adaptive, automatically adjusting its parameters and thresholds based on the specific characteristics of the data being processed. This dynamic approach allows the system to maintain high precision across diverse data types while preserving automated productivity
Data Source
AI summary
In particular embodiments, a data processing data inventory generation system is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include, for example, any entity that collects, processes, contains, and/or transfers personal data (e.g., such as a software application, “internet of things” computerized device, database, website, data-center, server, etc.). The system may be configured to identify particular data assets and/or personal data in data repositories using any suitable intelligent identity scanning technique.