SafeChat introduces a practical framework for creating LLM-based assistants that prioritize security, reliability, and trust.

• Trustworthiness by design: Implements source attribution, response filtering, and fail-safe mechanisms
• Traceable answers: Ensures responses can be validated against approved knowledge sources
• Strategic non-responses: Incorporates 'do-not-respond' capabilities for potentially harmful queries
• Practical implementation: Demonstrates real-world application through comprehensive case studies

This research addresses critical security concerns in AI deployment, making it possible to leverage LLM capabilities while maintaining organizational compliance and user safety standards.

SafeChat: A Framework for Building Trustworthy Collaborative Assistants and a Case Study of its Usefulness