EU AI Act 第10条 vs 第30条：你的数据团队需要知道什么
EU AI Act 第10条和第30条的详细对比——AI 训练数据治理、文档和合规方面最关键的两个条款。
第10条是关于准备训练数据的过程。第30条是关于输出——你必须产出和维护的文档。它们是互补的：第10条告诉你的数据管道必须做什么。第30条告诉你必须能证明它做了什么。
| 方面 | 第10条 | 第30条 |
|---|---|---|
| 焦点 | 如何准备数据 | 如何记录它 |
| 范围 | 数据治理实践 | 完整系统技术文档 |
| 时机 | 开发期间 | 贯穿整个生命周期维护 |
| 受众 | 内部团队 | 监管机构和当局 |
大多数企业的缺口不在于数据质量本身——而在于文档和可追溯性。统一的本地平台如 Ertas Data Suite 因为每个阶段共享相同的日志基础设施，所以将此文档作为正常操作的副产品生成。
Turn unstructured data into AI-ready datasets — without it leaving the building.
On-premise data preparation with full audit trail. No data egress. No fragmented toolchains. EU AI Act Article 30 compliance built in.
Keep reading
How On-Premise Data Preparation Solves EU AI Act Documentation Requirements
Why on-premise data preparation platforms naturally satisfy EU AI Act documentation requirements — and why cloud-based and fragmented pipelines create compliance gaps.
EU AI Act Article 10 Compliance: Data Prep Documentation as a Client Deliverable
How service providers turn EU AI Act Article 10 training data requirements into a structured client deliverable with documentation templates and deadlines.
Audit Trails for RAG Pipelines: What EU AI Act Article 30 Requires From Your Retrieval System
The EU AI Act mandates technical documentation and logging for high-risk AI systems. If your RAG pipeline feeds a high-risk application, every step from ingestion to retrieval needs an audit trail.