AI Assistant Expanding Security Context for Cloud Vulnerability Analysis
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Solution Overview
Problem
Current security solutions for AI systems lack the ability to expand their security context effectively, limiting their capability to analyze vulnerabilities and attack paths in cloud environments and CI/CD pipelines.
Innovation Solution
The integration of AI assistants with language models (LLM and SLM) that can receive user inputs, determine associated contexts, and communicate with language models to generate responses, thereby expanding the security context of AI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI assistants with language models are integrated to expand security context, then the security analysis capability and versatility are improved, but the device complexity increases
Solution Approach 1:
The patent introduces an AI assistant as an intermediary component that mediates between the security personnel and the complex security analysis systems. The AI assistant receives natural language queries, determines relevant security contexts, communicates with language models for analysis, and presents results in an understandable format. This intermediary layer shields users from the underlying system complexity while enabling versatile security context expansion through natural language interactions.
2Measurement precision
If multiple language models (LLM and SLM) are used to process security inquiries, then the measurement precision and analysis accuracy are improved, but the computing resources and time consumption increase
Solution Approach 1:
The patent segments the language model processing into two distinct components: a Large Language Model (LLM) for high-level security context understanding and query interpretation, and a Small Language Model (SLM) for specific vulnerability pattern recognition and detailed analysis. This segmentation allows each model to be optimized for its specific function, improving overall detection accuracy while managing computational resources more efficiently by distributing different types of processing tasks across models of appropriate sizes.
Data Source
AI summary
In one embodiment, a method includes receiving a selection of a UI element from a UI and determining a context associated with the UI element. The method also includes receiving an inquiry associated with the UI element and communicating the inquiry and the context to one or more language models. The method further includes receiving, by the one or more language models, a response to the inquiry using the inquiry and the context.