Large language models (LLMs) are powerful, but they can also be unpredictable and even dangerous. Imagine an LLM providing instructions on building a bomb—a terrifying thought. New research explores how to build "guardrails" around these models to prevent misuse while still allowing access for legitimate purposes. Traditionally, guardrails have been a one-size-fits-all approach, either blocking or allowing access to sensitive information. This new research proposes a more nuanced approach using a concept called "trust modeling." The idea is simple: give LLMs the ability to evaluate user trustworthiness and grant access accordingly. Similar to real-world security systems, LLMs would use factors like identity verification, past interactions, and even professional credentials to calculate a "trust score." Users with higher trust scores would have access to more sensitive data. The system leverages “in-context learning” to customize the LLM's responses to sensitive inquiries according to the user’s trust score, further enhancing safety. Experiments show promising results. The adaptive guardrail system significantly outperformed existing methods in granting access to sensitive information only to authorized users. For example, a computer science expert seeking information about complex algorithms would have greater access than someone with no relevant credentials. This trust-based approach achieves a better balance between access and security, making it a crucial step towards safer and more reliable LLM deployment. Challenges remain, such as defining universal trust metrics and handling evolving user behavior. But this research paves the way for building responsible and trustworthy next-generation AI systems that cater to diverse user needs without compromising safety.