Dynamic Sensitivity Tagging for Real-Time Access Control
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Solution Overview
Problem
Current digital communication platforms lack real-time sensitivity assessment and dynamic management of sensitive information, leading to misclassification, inadequate protection, and potential privacy breaches, while failing to comply with evolving data protection regulations.
Innovation Solution
A system utilizing natural language processing and machine learning for real-time sensitivity assessment, dynamic tagging, and controlled access management, incorporating predictive analytics to adjust permissions based on user roles, context, and regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static sensitivity tags are used in current systems, then implementation is simple, but misclassification of sensitive data occurs and protection is inadequate
Solution Approach 1:
The patent implements dynamic sensitivity tagging that automatically adjusts tags based on real-time contextual analysis of communication content, user roles, and data types. This transforms static tags into dynamic, context-aware classifications that adapt to changing conditions, resolving the contradiction between accuracy and complexity by using automated contextual analysis rather than manual static tagging.
Solution Approach 2:
The patent replaces manual or static sensitivity classification mechanisms with automated natural language processing and machine learning systems. These systems analyze communication context, sentiment, and content automatically to generate accurate sensitivity tags, substituting complex computational processes for simpler but inaccurate static tagging approaches.
2Measurement precision
If real-time contextual analysis is implemented, then sensitivity assessment accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements pre-processing steps that prepare communication data for analysis before full sensitivity assessment. By pre-identifying potential sensitive content types and organizing data structures in advance, the system reduces the computational burden during real-time analysis, thereby maintaining high accuracy while minimizing processing time delays.
Solution Approach 2:
The patent maintains continuous sensitivity monitoring and tagging as communications occur, rather than batch processing. This allows the system to provide real-time sensitivity assessment without significant delays by continuously analyzing and updating tags as data flows through the platform, ensuring both accuracy and timeliness.
3Reliability
If dynamic access control is implemented based on sensitivity tags, then data security improves, but system complexity and operational overhead increase
Solution Approach 1:
The patent implements automated access control systems that use sensitivity tags to automatically determine and enforce appropriate access permissions. The system self-manages access decisions by comparing user roles, clearances, and tag requirements without manual intervention, thereby maintaining high security while reducing operational overhead and simplifying user experience.
Solution Approach 2:
The patent incorporates feedback mechanisms where access control decisions are continuously monitored and adjusted based on sensitivity tag updates and user behavior patterns. This automated feedback loop ensures security requirements are met while reducing manual operational complexity by allowing the system to self-regulate access based on real-time conditions.
Data Source
AI summary
A method for real-time sensitivity assessment in a digital communication platform that includes receiving data inputs from one or more sources in one or more formats, pre-processing the received data inputs to normalize the data inputs, analyzing the pre-processed data inputs with a natural language processing (NLP) algorithm to determine sensitive information included in the data inputs, running a machine learning model on the sensitive information to determine a sensitivity level of the sensitive information based on context and sentiment of the sensitive information, generating sensitivity tags based on the determined sensitivity level, assigning the generated sensitivity tags to corresponding data inputs of the received data inputs, dynamically adjusting access permissions for users based on the assigned sensitivity tags, roles of the users, and contextual factors, and granting or denying access to the sensitive information based on the dynamically adjusted access permissions.