LLM-Based Topic Classification for Conference Access Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In corporate and government environments, conversations during meetings often shift topics without participants being aware of the security clearance levels of other attendees, leading to unauthorized access to sensitive information and potential data breaches.
Innovation Solution
Utilizing a large language model (LLM) to classify topics in real-time during communication sessions, determine security levels, and enforce access controls by alerting or removing unauthorized participants, muting audio/video, or obscuring content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time topic classification and security level determination are implemented using LLM, then security monitoring capability is improved, but system complexity and computational resource consumption increase
Solution Approach 1:
The patent introduces an LLM-based topic classification system as an intermediary between the communication session and the security monitoring mechanism. The LLM acts as a mediator that analyzes transcripts, determines security levels, and triggers appropriate security actions, thereby improving security monitoring capability while managing system complexity through modular architecture
Solution Approach 2:
The security monitoring system is segmented into distinct functional modules: transcript generation, topic classification, security level determination, and security action execution. This segmentation allows each component to be optimized independently and reduces overall system complexity by distributing computational tasks across multiple specialized subsystems
2Measurement precision
If continuous transcript analysis is performed during communication sessions, then detection precision of sensitive topics is improved, but processing time and computational resources increase
Solution Approach 1:
Instead of continuous real-time analysis of every word, the system periodically analyzes transcripts at meaningful intervals or when topic transitions are detected. This periodic action maintains high detection precision for sensitive topics while significantly reducing processing time and computational resource consumption compared to continuous analysis
Solution Approach 2:
The system performs preliminary topic classification on transcripts before full security analysis is triggered. This preliminary action allows the system to quickly identify potentially sensitive topics and apply more rigorous analysis only when necessary, thereby improving detection precision while minimizing overall processing time
3Speed
If automated security enforcement actions are implemented, then response speed to security breaches is improved, but potential false positives and operational disruptions increase
Solution Approach 1:
The system implements feedback mechanisms where security enforcement actions are monitored and evaluated. False positives are fed back into the training data for the LLM, allowing the system to learn from errors and improve its topic classification accuracy over time, thereby reducing false positives while maintaining fast response speed
Solution Approach 2:
Before implementing automated security enforcement actions, the system performs preliminary verification and confidence threshold checks. Topics that fall below certain confidence thresholds or match known false positive patterns are flagged for manual review before automated actions are taken, reducing the occurrence of false positives while maintaining rapid response capability for high-confidence detections
Data Source
AI summary
A transcript of a communication session is transmitted to a large language model (LLM) while the communication session is occurring. An indication of a security level associated with a current topic of the communication session is received from the LLM based on the transcript. It is determined whether a participant of the communication session is authorized to receive information associated with the current topic based on the security level associated with the current topic. One or more actions are performed when the participant is not authorized to receive the information associated with the current topic.