
本地数据准备如何解决 EU AI Act 文档要求
为什么本地数据准备平台自然满足 EU AI Act 文档要求——以及为什么基于云和碎片化的管道会造成合规差距。
EU AI Act 对高风险 AI 系统提出了严格的数据治理和文档要求。本文解释了为什么本地数据准备平台自然满足这些要求,以及为什么基于云和碎片化的管道会造成合规差距。
EU AI Act 第 10 条要求训练数据具有完整的来源追踪、质量评估文档、偏差检查记录和数据治理实践。集成式本地平台在每 个处理步骤自动生成这些审计产物,而使用 5-7 个独立工具拼接的管道无法产生统一的数据血统记录。
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

Best RAG Pipeline for Legal Documents: Privilege-Safe Retrieval With Full Audit Trail
Law firms and legal departments need document retrieval AI — but privileged documents cannot leave the building, and every access must be logged. Here is how to build a RAG pipeline that meets legal compliance requirements.

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.

EU AI Act Article 10 vs. Article 30: What Your Data Team Needs to Know
A detailed comparison of EU AI Act Articles 10 and 30 — the two most critical provisions for AI training data governance, documentation, and compliance.