[目的/意义]随着生成式人工智能对训练数据的依赖持续加深，超级平台凭借其结构性优势，在模型训练中逐步确立主导地位，构建起“平台-模型”共生格局。然而，现有研究较少关注训练数据环节中平台控制权的运行机制与治理困境。[方法/过程]采用规范分析与制度比较的方法，剖析“平台-模型”共生关系下独特的权力结构及风险类型，并揭示风险生成与治理路径。[结果/结论“] 共生”关系下的训练数据风险具有内生性与放大性特征，传统监管模式难以应对。为此，提出构建多方协同治理体系：通过原则性、分层级的政府主导来源记录框架应对隐私泄露与交叉风险；通过可验证的平台自评价体系破解治理壁垒风险；通过超级平台主导建设可信数据空间，化解数据流通风险。

[Purpose/significance] As generative artificial intelligence (GAI) grows more dependent on training data,super platforms have used their structural advantages to dominate model training, creating a "platform-model" symbiot⁃ic relationship. However, existing research has paid little attention to the operational mechanisms of platform control and the associated governance dilemmas within the training data phase. [Method/process] Using normative analysis and institutional comparison, this paper dissects the unique power structure and risk types inherent to the "platformmodel" symbiosis and reveals their risk formation and governance pathways. [Result/conclusion] The study finds that training data risks under this symbiotic relationship are endogenous and amplified, challenging traditional regulatory models. To address this, it proposes a multi- stakeholder collaborative governance system featuring: a principled,tiered, government-led source documentation framework to manage privacy leakage and data crossover risks; a verifi⁃able platform self-assessment system to dismantle governance barriers; and superplatform-led construction of trusted data spaces to mitigate data circulation risks.