LLM Trust Layer for Data Privacy and Content Moderation
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Solution Overview
Problem
Cloud platforms face challenges in maintaining data privacy and security when accessing machine learning models, particularly with sensitive data and the risk of generating unsafe content.
Innovation Solution
A model interface is implemented to moderate content for input into and generated by large language models, including masking sensitive information and defending against adversarial prompts, with customizable content moderation based on configuration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a cloud platform uses a multi-tenant database system to support multiple users and applications, then the platform can provide shared resources and collaborative functionality, but data privacy and security risks increase due to sensitive data exposure
Solution Approach 1:
The patent introduces a trust layer as an intermediary component between the multi-tenant database system and machine learning models. This trust layer intercepts data before it reaches external models, masks sensitive information, and filters harmful content, thereby enabling multi-tenant versatility while mitigating data privacy risks through controlled data exposure
2Productivity
If the cloud platform allows direct access to machine learning models for various tasks, then productivity and functionality improve, but unsafe content generation and data leakage risks increase
Solution Approach 1:
The trust layer performs preliminary actions by masking sensitive data and filtering harmful content before it reaches the machine learning model. This pre-processing ensures that only safe and sanitized data is input into the model, preventing unsafe content generation while maintaining efficient model access for various tasks
Solution Approach 2:
The trust layer implements feedback mechanisms to monitor and evaluate model outputs for harmful content. When unsafe content is detected, the system can block the output, alert administrators, or adjust the input data, creating a feedback loop that maintains productivity while preventing harmful generations
3Object-affected harmful factors
If the cloud platform implements comprehensive content moderation and data masking, then data privacy and security improve, but system complexity and processing overhead increase
Solution Approach 1:
The trust layer segments the content moderation and data masking functionality into a separate, dedicated component. This segmentation allows the trust layer to handle security tasks independently without complicating the entire system architecture, as it operates as a modular layer between data storage and model access
Data Source
AI summary
A cloud platform may include a model interface that receives from a client and at an interface for accessing a large language model, a prompt for a response from the large language model, and the client is associated with a set of configuration parameters via a cloud platform that supports the interface. The cloud platform may modify, in accordance with the set of configuration parameters, the prompt that results in a modified prompt and transmit, to the large language model, the modified prompt. The cloud platform may receive the response generated by the large language model and provide the response to a model that determines one or more probabilities that the response contains content from one or more content categories. The cloud platform may transmit the response or the one or more probabilities to the client.