Private Cloud AI Platform With Local RAG for Secure LLM Access
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Solution Overview
Problem
The deployment of large language models (LLMs) in customer environments is hindered by security concerns and network limitations, particularly when running these models remotely, which can introduce data transfer risks and access control issues.
Innovation Solution
An integrated private cloud AI platform is deployed at the customer site, utilizing a retrieval-augmented generation (RAG) system with a generative AI chatbot module, embedding models, and vector-based information retrieval to enhance data storage, security, and authentication, allowing for secure access and management of LLMs within a private network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are deployed in remote systems, then accessibility and computational power are improved, but security concerns and data transfer risks worsen
Solution Approach 1:
The patent introduces a private cloud platform as an intermediary between the customer's local systems and remote LLM services. This intermediary enables secure deployment of LLMs within the customer's controlled environment while maintaining the benefits of advanced AI capabilities, thereby resolving the contradiction between accessibility and security risks
Solution Approach 2:
The patent implements copying of LLM components and data into the customer's private cloud environment. By creating local copies of the models and data within the customer's controlled infrastructure, the system eliminates the need for continuous data transfer over open networks, thus maintaining security while providing accessible LLM functionality
2Reliability
If LLMs are downloaded to customer sites, then local deployment and security are improved, but network bandwidth and download time worsen
Solution Approach 1:
The patent employs preliminary action by pre-processing and preparing LLM components, data, and computational resources in advance within the private cloud platform. This includes pre-downloading model components, pre-configuring computational environments, and pre-establishing security protocols, thereby reducing the actual download and deployment time while maintaining security
Solution Approach 2:
The patent applies segmentation by dividing the LLM into manageable components and modules that can be selectively downloaded and deployed. Instead of transferring the entire massive model at once, the system segments the model into smaller parts that can be efficiently transferred and instantiated as needed, significantly reducing download time and network bandwidth requirements
3Productivity
If remote systems are shared with other entities, then resource utilization is improved, but access control and data privacy worsen
Solution Approach 1:
The patent implements local quality by customizing the deployment environment to match the specific needs and security requirements of each customer. Each customer receives a dedicated private cloud environment with tailored access controls, data handling procedures, and security configurations, thereby maintaining strong data privacy while efficiently utilizing shared infrastructure resources
Data Source
AI summary
Systems and methods are provided for an integrated private cloud AI platform that is deployable at a customer site. The integrated private cloud AI platform can include components deployed at the customer site that utilize an embedding model that accesses/integrates with various knowledge bases and vector data stores locally at the customer site, and permit chatbot-accessible queries to an LLM that utilizes the previously-uploaded embeddings.