SafeGPT Guardrail System Prevents Data Leakage in Enterprise LLM Use
A recent study introduces SafeGPT, a dual-layer safety mechanism aimed at stopping the unauthorized sharing of sensitive information and unethical results when businesses utilize large language models (LLMs). This system combines detection and redaction on the input side with moderation and reframing on the output side, along with human feedback. Experimental results indicate that SafeGPT significantly lowers the chances of data leakage and biased results while ensuring user satisfaction remains high. The research is accessible on arXiv in the Cryptography and Security section.
Key facts
- SafeGPT is a two-sided guardrail system for enterprise LLM use.
- It prevents sensitive data leakage and unethical outputs.
- The system includes input-side detection/redaction and output-side moderation/reframing.
- Human-in-the-loop feedback is integrated into SafeGPT.
- Experiments show reduced data leakage risk and biased outputs.
- User satisfaction is maintained with SafeGPT.
- The paper is titled 'SafeGPT: Preventing Data Leakage and Unethical Outputs in Enterprise LLM Use'.
- It is published on arXiv under Computer Science > Cryptography and Security.
Entities
Institutions
- arXiv