Data Inventory System for Privacy Compliance
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Solution Overview
Problem
Current systems lack effective methods to manage and comply with privacy and security policies regarding personal data, leading to frequent breaches and unauthorized access, necessitating improved systems for data inventory generation and compliance with legal and industry standards.
Innovation Solution
A data processing system that generates and populates a data model by identifying primary and transfer data assets, storing inventory attributes, and electronically linking these assets to ensure compliance with privacy and security regulations, utilizing processors, computer memory, and computer-readable instructions to manage personal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data inventory management methods are used, then implementation simplicity is maintained, but compliance reliability with privacy and security policies deteriorates
Solution Approach 1:
The patent segments data inventory management into distinct functional modules including data asset identification, inventory attribute generation, transfer data tracking, and electronic linking components. Each module handles specific aspects of data management, allowing the system to achieve comprehensive compliance monitoring through divided, specialized functions rather than a monolithic approach.
Solution Approach 2:
The system performs preliminary actions by automatically generating data inventories and establishing electronic links between data assets before compliance issues arise. The automated identification and cataloging of personal data occurrences happen proactively, enabling organizations to demonstrate compliance readiness and prevent breaches rather than reacting to compliance failures.
2Reliability
If comprehensive data tracking is implemented, then compliance monitoring is improved, but data retrieval efficiency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors data asset relationships and automatically updates the data inventory. The electronic linking structure provides real-time feedback about data flows and transfers, enabling compliance monitoring without manual intervention while maintaining efficient data retrieval through automated, structured tracking.
Solution Approach 2:
The system creates structured copies of data relationship information in the form of electronic links and inventory attributes. Rather than tracking actual data movements which would impact performance, the system maintains replicated metadata about data assets and their relationships, enabling efficient compliance monitoring through these lightweight copies.
3Measurement precision
If automated data inventory generation is used, then compliance accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary automated identification and cataloging of data assets, generating inventory attributes before compliance audits or breaches occur. This proactive approach ensures accurate compliance documentation is already in place, eliminating the need for time-consuming manual inventory creation when compliance issues arise.
Solution Approach 2:
The automated system performs self-service data inventory generation by independently identifying personal data occurrences, generating inventory attributes, and establishing electronic links without requiring manual intervention. This self-automating process maintains high compliance accuracy while reducing the time investment required from human operators.
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.). For example, a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.