Network Security Gateway Using LLMs for Natural-Language Inspection
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Solution Overview
Problem
Existing communications between client devices and remote applications, particularly those using Generative AI, face security risks such as access control challenges, data leakage, generation of harmful content, and lack of visibility into audit logs, leading to difficulties in managing user behavior and compliance.
Innovation Solution
A network security gateway deployed between client devices and servers uses large language models to monitor and perform security operations on natural language data, providing enhanced data traceability, granular user activity classification, and real-time threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a security gateway is deployed to monitor communications between client devices and remote applications, then security risks such as data leakage and access control are improved, but network latency and processing time increase due to the need to inspect and analyze natural language data using large language models
Solution Approach 1:
The system performs preliminary classification of communications into categories (e.g., safe, suspicious, malicious) using the LLM before full security processing. This preliminary action allows the system to prepare security responses in advance and only perform intensive inspection on communications that require it, reducing overall latency while maintaining security effectiveness
Solution Approach 2:
The security gateway processes communications in segmented stages: initial filtering, LLM-based analysis of natural language data, security policy evaluation, and response generation. This segmentation allows parallel processing of different communication streams and prevents bottlenecking, reducing network latency while maintaining comprehensive security monitoring
2Measurement precision
If the security gateway uses large language models to inspect natural language data in real-time, then detection precision of harmful content is improved, but computational resource consumption and system complexity increase
Solution Approach 1:
The patent introduces a policy server as an intermediary between the LLM and the security gateway. The policy server manages LLM invocations, stores security policies, and coordinates analysis requests. This intermediary layer simplifies the overall system architecture by centralizing complex LLM management and policy coordination, reducing system complexity while maintaining high detection precision through comprehensive LLM-based analysis
3Measurement precision
If the security gateway performs comprehensive analysis of user activities and data exchanges, then user behavior classification and threat detection are improved, but data privacy concerns and potential exposure of sensitive information increase
Solution Approach 1:
The security gateway applies different levels of inspection and analysis to different types of data based on their sensitivity and risk profiles. Sensitive personal information receives enhanced protection with stricter handling protocols, while less sensitive data undergoes standard analysis. This localized quality approach allows comprehensive user behavior classification while minimizing unnecessary exposure of sensitive information, balancing detection precision with data privacy protection
Data Source
AI summary
A security gateway accesses data in a communications session between a client device and an application hosted by a server. The security gateway inspects security parameters corresponding to the data using one or more large language models (LLMs). In response to inspecting the security parameters corresponding to the data, the security gateway performs one or more security operations on the data in accordance with one or more security policies associated with the one or more LLMs.