Trustworthy AI: Securing Sensitive Data in Large Language Models

LLMs pose real privacy risks in sensitive fields: The paper identifies that while Large Language Models excel at text generation and understanding, their use in healthcare, finance, and legal services creates "critical concerns about privacy and data security" that require active safeguards rather than relying on the models alone.

A three-part trust system can control what information gets shared: The proposed framework uses User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control to match what information is revealed to who is asking for it—similar to how a doctor might share different details with a patient versus a specialist depending on their role.

The system balances usefulness with privacy protection: By integrating techniques like differential privacy and role-based access controls, the framework aims to keep LLMs functional and helpful while preventing inappropriate disclosure of sensitive data, addressing a key tension in responsible AI deployment.

Automated detection of sensitive information is central to the solution: The framework uses Named Entity Recognition and contextual analysis to automatically identify what counts as sensitive information in real-time, reducing reliance on manual oversight that could miss risks.

Real-world testing across industries is still needed: The authors acknowledge that while the framework is theoretically sound, its actual effectiveness "across various domains" remains to be demonstrated, meaning organizations should await validation results before full implementation.