AI-Segmented Document Access for Section-Level Security
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Solution Overview
Problem
Current document security methods lack the granularity to control access at the individual page or section level within documents, leading to potential unauthorized access to sensitive information.
Innovation Solution
Implementing AI-driven tagging of document segments with unique identifiers and an access control table to manage permissions at a granular level, allowing users to access only authorized sections based on their roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document security methods are used, then document-level access control is provided, but granular control at page or section level is lost
Solution Approach 1:
The patent divides a document into multiple segments (pages, sections, or other logical divisions) and assigns unique identifiers to each segment. This segmentation enables granular access control at the segment level rather than treating the entire document as a single unit, thereby improving access control precision without requiring complete system redesign
Solution Approach 2:
The patent introduces an intermediary component (security software or processing system) that receives the document, segments it, assigns identifiers, and manages access control tables. This intermediary handles the complexity of granular security management, allowing the core document system to remain relatively simple while achieving fine-grained access control
2Reliability
If granular access control is implemented, then unauthorized access to sensitive information is reduced, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by automatically segmenting the document and assigning unique identifiers to each segment before access control is enforced. The access control table is pre-configured with permissions for each segment identifier, which simplifies the actual access enforcement process and reduces runtime complexity while maintaining high security reliability
Solution Approach 2:
The patent creates a simplified representation or copy of the document structure in the form of an access control table that maps segment identifiers to permission levels. This tabular representation copies the essential security metadata without duplicating the entire document, enabling efficient access decisions without handling the full document complexity
3Productivity
If AI-driven tagging is used, then automated segment identification is achieved, but processing time increases
Solution Approach 1:
The patent employs AI-driven models that automatically analyze the document content, identify logical segments, and assign unique identifiers without requiring manual intervention. The system serves itself by autonomously performing the segmentation and tagging tasks, which improves productivity by eliminating manual labor while the automated nature of the process ensures consistent and rapid execution
Solution Approach 2:
The patent utilizes AI models that can adjust processing parameters such as segment size thresholds, identification criteria, and tagging strategies based on document characteristics. By dynamically changing these parameters, the system optimizes the balance between automation level and processing time, achieving high productivity without excessive time loss
4Measurement precision
If permissions are defined for each segment, then access control precision is improved, but configuration effort increases
Solution Approach 1:
The patent creates a universal access control table structure that can accommodate any document segmentation scheme and permission requirement. The tabular format with standardized columns (segment identifier, user/role, permission level) provides a multi-functional framework that handles diverse access control scenarios through a single consistent interface, improving configuration ease despite the precision required
Data Source
AI summary
Embodiments relate to providing granular level document security with role-based access. A technique includes determining, by a first artificial intelligence (AI) model, segments of a document and generating, by a second AI model, tags for the segments of the document, the tags being unique identifiers for the segments. The technique includes defining permissions for the tags in an access control table based on a plurality of identifications, an identification being in the plurality of identifications. The technique includes, in response to receiving the identification with a request to access the document, extracting the tags permitted for the identification from the access control table, and filtering the document according to the segments for the tags permitted for the identification. The technique includes, in response to the request to access the document, causing the document that is filtered to be rendered with the segments for the tags permitted for the identification.