OneShield -- the Next Generation of LLM Guardrails

TL;DR

OneShield is a customizable, model-agnostic framework designed to enhance the safety, privacy, and ethical compliance of large language models by allowing tailored risk management and policy enforcement.

Contribution

It introduces a novel stand-alone solution that addresses the challenge of safeguarding diverse LLMs through flexible, user-defined policies and risk mitigation strategies.

Findings

Successfully deployed with usage statistics since launch

Supports defining risk factors and safety policies

Scalable and adaptable to different models

Abstract

The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective measures for their own models and standalone solutions. The constantly evolving nature of LLMs makes it extremely challenging to universally shield users against their potential risks, and one-size-fits-all solutions are unfeasible. In this work, we propose OneShield, our stand-alone, model-agnostic and customizable solution to safeguard LLMs. OneShield aims to provide facilities for defining risk factors, expressing and declaring contextual safety and compliance policies, and mitigating LLM risks, with a focus on each specific customer. We describe the implementation of the framework, discuss scalability considerations, and provide usage statistics of OneShield since its initial deployment.