生成式 AI 工作流中的特权与保密性分析
Privilege and confidentiality in generative AI workflows
AI 导读
一篇 arXiv 论文分析生成式 AI 系统存储与处理客户数据的三种方式——模型参数中的训练记忆、会话上下文窗口、以及 RAG 用的知识库，指出每种模式带来不同的保密与法律职业特权风险。论文基于英国 Munir 案与美国 Heppner 案等首批相关判例，结合英格兰和威尔士律师监管框架，论证有效的信息治理标准正在变化。
正文 · 原文
Abstract:Generative AI (GenAI) systems store and process client data in three distinct ways: in the model's parameters through training and memorisation, in the context window during a live session, and in knowledge databases for retrieval-augmented generation (RAG). Each mode creates different and often counter-intuitive risks to confidentiality and legal professional privilege, and each calls for specific governance responses. Drawing on the first English and American decisions to address privilege and generative AI, UK and Munir v Secretary of State for the Home Department and United States v Heppner, on the orthodox privilege authorities against which those decisions must be read, and on recent computer science research, we explain the three modes of data storage and processing in terms accessible to practitioners and analyse the legal consequences of each. We then situate the analysis within the regulatory framework governing solicitors in England and Wales and within the ordinary principles of professional negligence, arguing that the standard of effective information governance (and with it the benchmark against which negligence and misconduct will be measured) is changing. Although we write primarily for SRA-regulated practitioners, our data-governance analysis is framed to extend to any jurisdiction in which the protection of privilege or professional secrecy depends on demonstrable confidentiality. The ultimate aim of this article is to help legal services professionals understand salient data leakage risks in GenAI systems and thereby facilitate a more responsible deployment of GenAI on client data and other sensitive material.
| Comments: | Version with minor amendments |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.05479 [cs.CR] |
| (or arXiv:2607.05479v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05479 arXiv-issued DOI via DataCite |
Submission history
From: Thomas Melham [view email]
[v1]
Mon, 6 Jul 2026 14:38:58 UTC (566 KB)
[v2]
Wed, 11 Nov 2026 16:44:14 UTC (640 KB)
来源：arXiv cs.AI · arxiv.org