LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance

Large language models (LLMs) are increasingly applied in fields such as finance, education, and governance due to their ability to generate human-like text and adapt to specialized tasks. However, their widespread adoption raises critical concerns about data privacy and security, including the risk of sensitive data exposure.In this paper, we propose a security framework to enforce policy compliance and mitigate risks in LLM interactions. Our approach introduces three key innovations: (i) LLM-based policy enforcement: a customizable mechanism that enhances domain-specific detection of sensitive data. (ii) Dynamic policy customization: real-time policy adaptation and enforcement during user-LLM interactions to ensure compliance with evolving security requirements. (iii) Sensitive data anonymization: a format-preserving encryption technique that protects sensitive information while maintaining contextual integrity. Experimental results demonstrate that our framework effectively mitigates security risks while preserving the functional accuracy of LLM-driven tasks.

@article{wang2025_2505.17145,
  title={ LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance },
  author={ Yu Wang and Cailing Cai and Zhihua Xiao and Peifung E. Lam },
  journal={arXiv preprint arXiv:2505.17145},
  year={ 2025 }
}