Multimodal Content Interpretation for Network Security Policy Enforcement
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Solution Overview
Problem
Existing content interpretation techniques in computer networks are limited to text-only data and struggle to detect unauthorized information in multimodal data formats such as images and videos, leading to potential data breaches and security vulnerabilities.
Innovation Solution
Utilizing large-language model (LLM) agents to determine the semantic context of multimodal data, allowing for the application of network security policies to identify and block unauthorized information across various data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-only content interpretation techniques are used, then the system is simple to implement, but it cannot detect unauthorized information in multimodal data formats such as images and videos
Solution Approach 1:
The patent implements a universal content interpretation system that handles multiple data formats (text, images, videos, audio) through a unified architecture. The system uses modality-specific processors that convert different data types into standardized representations, allowing a single policy enforcement engine to apply security policies across all formats. This multi-functional design enables the system to detect unauthorized information in various formats without requiring separate specialized systems for each modality.
2Measurement precision
If semantic analysis of multimodal data is performed, then detection accuracy improves, but processing load and resource consumption increase
Solution Approach 1:
The patent divides the content interpretation process into distinct modular components: text processing module, image processing module, video processing module, and audio processing module. Each module is specialized for its data type and can be independently configured and optimized. This segmentation allows the system to apply appropriate processing intensity to each modality based on security requirements, reducing overall processing load while maintaining detection accuracy.
Solution Approach 2:
The system implements selective semantic analysis that applies full-depth processing only to data types or content categories with higher security risks. For lower-risk content, the system uses lighter processing methods. This partial action approach ensures high detection accuracy for critical threats while reducing unnecessary processing load for routine content, optimizing the balance between security and resource consumption.
Data Source
AI summary
A method of managing a computer network includes: receiving, at a network port, a stream of multimodal data; obtaining, from the multimodal data, a subset of the multimodal data that corresponds to a modality; determining, using a large-language model (LLM) agent, a semantic context of the subset of the multimodal data; determining, based on the semantic context and among a plurality of network policies, a network security policy corresponding to the subset of the multimodal data; and directing the subset of the multimodal data according to the network security policy.