Adaptive Language Model for Privacy-Preserving Security Insights
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Solution Overview
Problem
Traditional security and safety management approaches are reactive and insufficient in addressing the challenges posed by the rapid proliferation of connected devices and increasing data generation, particularly in providing real-time insights and adapting to diverse user needs.
Innovation Solution
An adaptive language model-based solution that leverages natural language processing and situational awareness to provide real-time insights, while adapting to user needs by hard fine-tuning large language models to achieve near-perfect accuracy in recognizing user intent, even with unpredictable outcomes from generative AI algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reactive security management approaches are used, then system simplicity is maintained, but real-time insight capability and adaptability to user needs deteriorate
Solution Approach 1:
The system segments the complex security management task into distinct functional modules: a natural language processing module that converts user queries into machine syntax, a data processing module that analyzes security data, and a response generation module that delivers insights. This segmentation allows the system to achieve high adaptability through modular components while managing complexity through organized separation of concerns.
Solution Approach 2:
The patent introduces an intermediary natural language processing layer that mediates between user intent and complex security data analysis. This intermediary converts diverse natural language queries into standardized machine query syntax, enabling the system to adapt to various user needs without requiring complex direct interpretation logic, thus improving adaptability while controlling system complexity.
2Measurement precision
If natural language queries are processed by large language models with direct data access, then user intent recognition accuracy improves, but data privacy deteriorates
Solution Approach 1:
The system extracts and processes only the essential query intent from natural language inputs, separating the critical information needed for accurate intent recognition from the underlying sensitive data. By taking out only the necessary semantic meaning and converting it to machine query syntax without exposing raw data to external language models, the system achieves high recognition accuracy while preserving data privacy.
Solution Approach 2:
The patent employs an intermediary processing layer that acts as a barrier between sensitive security data and external large language models. This intermediary converts natural language queries into standardized machine syntax and processes requests without transmitting underlying sensitive data to external models, thereby maintaining both accurate intent recognition and data privacy protection.
3Productivity
If generative AI algorithms are used for security analysis, then insight generation capability improves, but outcome predictability deteriorates
Solution Approach 1:
The system performs preliminary actions by converting natural language queries into standardized machine query syntax before submitting requests to generative AI models. This preliminary structuring of queries ensures that the insights generated are directly relevant to specific security concerns, improving both productivity in insight generation and reliability by reducing the unpredictability inherent in free-form generative AI responses.
Data Source
AI summary
An interface provides answers to natural language user queries based upon real time data generated by live processes. The natural language queries are converted into a machine query syntax and the machine query syntax is provided to a large language model without sharing underlying data that is used to satisfy the natural language query by serving the data to the end user while, at the same time, masking the data from large language model, where interaction with the large language model is based upon a predefined syntax protocol. A reply is a received from the large language model in the same syntax and the reply is used to create an output to be served to the end user and/or to execute a functionality.