Date
Publisher
arXiv
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.
Study at a glance
- Question: How can a security framework protect sensitive data and enforce privacy policies during interactions with large language models?
- Setting and data: Not stated in the abstract
- Method: Development and experimental testing of a security framework
Why it matters
As large language models are increasingly used in fields such as education, their widespread adoption raises critical concerns about data privacy, security, and the risk of sensitive data exposure.
Key findings
- A security framework effectively reduces data privacy risks without sacrificing the functional accuracy of language model tasks in fields like education.
- The framework adapts and enforces privacy rules in real time during user interactions to meet evolving security requirements.
- Anonymizing sensitive data with specialized encryption protects confidential information while preserving the context required for language models to function.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
