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arXiv · 2601.06366

SafeGPT: Preventing Data Leakage and Unethical Outputs in Enterprise LLM Use

Pratyush Desai·Luoxi Tang·Yuqiao Meng·Zhaohan Xi
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Abstract

Large Language Models (LLMs) are transforming enterprise workflows but introduce security and ethics challenges when employees inadvertently share confidential data or generate policy-violating content. This paper proposes SafeGPT, a two-sided guardrail system preventing sensitive data leakage and unethical outputs. SafeGPT integrates input-side detection/redaction, output-side moderation/reframing, and human-in-the-loop feedback. Experiments demonstrate SafeGPT effectively reduces data leakage risk and biased outputs while maintaining satisfaction.

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Pratyush Desai, Luoxi Tang, Yuqiao Meng, Zhaohan Xi. 2026-01-10. SafeGPT: Preventing Data Leakage and Unethical Outputs in Enterprise LLM Use. https://arxiv.org/abs/2601.06366

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