跨境AI训练数据流动的法律风险与企业治理机制研究
生成式人工智能快速发展使训练数据成为影响模型能力与产业竞争力的重要要素，训练数据在采集、标注、存储、训练与调用中的跨境流动亦日益常态化。围绕这一过程，个人信息保护、数据出境程序、知识产权、合作链条责任分配与模型输出风险相互叠加。本文以AI训练数据处理链条为基础，采用规范分析、类型化分析与场景化分析方法，梳理训练数据跨境流动中的个人信息处理、数据出境程序、来源合法性、委托处理与模型输出风险，并进一步区分境外标注、远程访问、境外算力协作和集团内部调拨等典型场景。本文认为，现行规则虽已形成基本框架，但对训练数据跨境场景中的法律属性识别、境外接收方约束和供应链追溯仍回应不足。对此，应围绕数据分类分级、来源审查、跨境路径识别、合同控制、质量管理与审计追溯建立分层治理机制，以提高训练数据跨境流动的合规确定性。
收录于中国知网，会议名称：2026年民商法主题青年研讨会，会议时间：2026-03-15，会议地点：中国北京。DOI：10.26914/c.cnkihy.2026.019606。作者：牟宁、韩硕。
Legal Risks and Enterprise Governance Mechanisms for Cross-border AI Training Data Flow
This conference paper examines the legal risks and enterprise governance mechanisms associated with cross-border flows of AI training data. Starting from the AI training data processing chain, it analyzes risks related to personal information processing, data export procedures, source legality, entrusted processing, and the downstream risks arising from model outputs. It further distinguishes typical scenarios such as overseas annotation, remote access, overseas computing collaboration, and intra-group data transfers, and proposes layered governance mechanisms around data classification, source review, cross-border path identification, contractual controls, quality management, auditability, and risk response.
Indexed by CNKI. Conference: 2026 Youth Seminar on Civil and Commercial Law. Conference date: March 15, 2026. Location: Beijing, China. DOI: 10.26914/c.cnkihy.2026.019606. Authors: Mou Ning and Han Shuo.