Large language models (LLMs) are revolutionizing how we interact with technology, but their ability to access and process vast amounts of data raises serious concerns about the security of sensitive information. What if these powerful AI tools could inadvertently leak private data? This is a real concern, especially in sensitive fields like healthcare, finance, and law. A new research framework offers a solution by building trust directly into how LLMs operate. Imagine an AI system that acts like a discreet gatekeeper, carefully evaluating who should access sensitive information. This framework does just that, creating user trust profiles based on roles, purpose of access, and even the security of their device and network. It's like having a personalized security system for your data within the AI itself. But how does it work? The system employs a combination of techniques, including something called Named Entity Recognition (NER). NER acts like a highly trained detective, scanning text for sensitive data like names, addresses, and medical records. It then flags this information, ensuring it’s handled with extra care. The system goes even further, using contextual analysis to understand the nuances of language and identify sensitive information hidden within broader discussions. This added layer of protection prevents leaks that traditional keyword filters might miss. Once sensitive information is identified, the system decides what to do based on the user's trust level. For users with lower trust scores, sensitive data is automatically redacted or summarized, providing only essential information without revealing private details. High-trust users, like doctors or lawyers, may be granted access to the full information they need to do their jobs. This adaptive output control ensures that information is shared responsibly, minimizing the risk of unauthorized access. The framework also leverages techniques like differential privacy, adding a carefully calculated layer of “noise” to data, preventing the model from memorizing and leaking sensitive information. This technique makes it incredibly difficult for malicious actors to reconstruct private data from the model’s output. This research marks a significant step towards building more trustworthy AI systems. It recognizes that security isn’t just about protecting data; it's also about ensuring that the right people have access to the information they need. By weaving trust directly into the fabric of AI, we’re not only safeguarding sensitive data, but also building a more secure and reliable future for this transformative technology.