Dynamic Modular Data Classification Engine for Section-Level Access Control
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Solution Overview
Problem
Existing data classification systems are prone to mislabeling and inconsistent labeling of data, leading to unauthorized access and restricted information flow due to reliance on human classification and duplication of documents.
Innovation Solution
A data scanning system that automatically classifies data files based on content analysis, performs modular classification of data sections, and implements dynamic access control to ensure secure data sharing and transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual classification by human users is used, then ease of operation is improved, but measurement precision deteriorates due to mislabeling
Solution Approach 1:
The system enables self-service classification by automatically analyzing document content and assigning classification labels without requiring human intervention. The machine learning model processes document metadata and content to autonomously determine classification, eliminating manual labeling while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical human classification process with an automated electronic system. The machine learning model substitutes for human analysts, using computational algorithms to process documents and assign classifications, thereby eliminating human error while maintaining operational efficiency.
2Productivity
If multiple copies of documents are created and stored, then productivity is improved, but reliability deteriorates due to inconsistent labeling
Solution Approach 1:
The system segments the classification task by processing each document copy independently through the machine learning model, ensuring that each copy receives consistent classification based on its content. This segmentation approach allows multiple copies to be handled simultaneously while maintaining uniform labeling standards.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously refines its classification based on document content analysis. This feedback loop ensures that even when multiple copies are created, the classification remains consistent and reliable, as the model adapts to the specific content of each document.
3Reliability
If strict access control is implemented, then security is improved, but ease of operation deteriorates due to restricted information flow
Solution Approach 1:
The access control system is made dynamic by automatically adjusting permissions based on the classification of document content. The system analyzes document metadata and content to determine appropriate access levels, allowing flexible information sharing that adapts to the sensitivity of each document rather than applying static restrictions.
Solution Approach 2:
The system changes access control parameters dynamically based on document classification. By analyzing content and metadata, the system adjusts permission settings to match the sensitivity level of each document, enabling secure access for authorized users while maintaining ease of operation for appropriate information sharing.
Data Source
AI summary
Methods and systems are presented for providing a data control framework that enables storing, sharing, and transferring of data in a secure manner. Data files stored in data repositories are scanned. Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file. Each data file is also assigned to a clearance classification based on an expected viewer of the data file. When sections from a first data file is being transferred to a second data file, a data control mechanism is triggered. If a particular section from the first data file is incompatible with the second data file, the data control mechanism may prevent the particular section from being transferred to the second data file, while allowing the remaining sections being transferred to the second data file.