LLM-Shield: Privacy Proxy for Cloud LLMs

LLM-Shield is a privacy proxy designed for those using cloud-based language models while concerned about client data privacy. It offers two modes: Mask Mode, which anonymizes personal identifiable information (PII) such as emails and names before sending data to OpenAI, and Route Mode, which keeps PII local by routing it to a local language model. The tool supports various PII types across 24 languages with automatic detection, utilizing Microsoft Presidio. Easily integrated with applications using the OpenAI API, LLM-Shield is open-sourced and includes a dashboard for monitoring. Future enhancements include a Chrome extension for ChatGPT and PDF/attachment masking. This matters because it provides a solution for maintaining data privacy when leveraging powerful cloud-based AI tools.

In an era where data privacy is paramount, the introduction of LLM-Shield offers a significant advancement for those utilizing cloud-based large language models (LLMs) while being concerned about the exposure of sensitive client data. The tool functions as a privacy proxy, providing two distinct modes to handle personally identifiable information (PII). The Mask Mode allows users to send data to cloud LLMs without revealing sensitive details, as it replaces PII with placeholders before transmission. This ensures that client data such as names, emails, and other identifiers remain protected while still allowing the LLM to process the request. The original information is restored upon receiving the response, maintaining the integrity of the communication.

The Route Mode offers another layer of privacy by directing requests containing sensitive data to local LLM setups instead of cloud-based services. This mode is particularly beneficial for organizations that have the infrastructure to support local LLMs and prefer to keep PII entirely within their control. By using Microsoft Presidio for entity detection, LLM-Shield can accurately identify and manage various types of PII across multiple languages, making it a versatile tool for global applications. This capability is crucial for businesses operating in diverse linguistic environments, ensuring compliance with local data protection regulations.

LLM-Shield’s integration with the OpenAI API makes it accessible for a wide range of applications, from web interfaces to custom scripts. The ease of setup, requiring only a few steps to clone the repository and configure the proxy, means that even those with limited technical expertise can implement this privacy solution. Additionally, the inclusion of a dashboard for monitoring provides users with insights into the proxy’s operations, allowing for real-time adjustments and optimizations. This transparency is vital for maintaining trust and ensuring that the tool meets the organization’s privacy standards.

The ongoing development of LLM-Shield, with plans for a Chrome extension and enhanced features like PDF and attachment masking, highlights the evolving nature of privacy requirements in the digital age. As more organizations recognize the importance of safeguarding client data, tools like LLM-Shield become indispensable. Feedback on detection accuracy and suggestions for additional entity types will be crucial for refining the tool’s capabilities, ensuring it remains a robust solution for privacy-conscious users. This matters because as data privacy laws become more stringent, having reliable tools to manage and protect sensitive information will be essential for compliance and maintaining client trust.

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