为向量数据库设计细粒度访问控制机制，解决安全与检索效率的矛盾。
Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases
- 将细粒度访问策略形式化为向量库中的可执行规则
- 揭示了访问控制、召回率与查询延迟间的内在权衡
- 适合关注AI安全与数据隐私的研究者与工程师
向量数据库在安全敏感场景（如检索增强生成和组织级AI流程）中应用日益广泛，但其安全能力仍有限。当前现代向量数据库尚不完善支持细粒度访问控制（FGAC），而该功能对确保数据访问符合用户特定策略至关重要。与关系型数据库不同，向量数据库融合结构化与非结构化属性，提供语义近似查询结果，使FGAC实现复杂化。这导致在正确执行FGAC策略、保持高近似最近邻（ANN）搜索召回率以及维持低查询延迟之间存在根本性矛盾。本文提出面向策略的向量搜索愿景，形式化向量数据库中FGAC策略模型及其执行问题，比较多种执行策略，呈现初步发现，并识别未来研究的关键开放挑战。
原文摘要 · Abstract (English)
Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. Specifically, Fine-grained Access Control (FGAC) which is required to ensure that data access adheres to user-specific policies is not fully supported in modern vector databases. Unlike relational databases, vector databases combine structured and unstructured attributes to provide semantic, approximate query results, which complicates FGAC implementation. This creates an inherent tension between enforcing FGAC policies correctly, achieving high ANN search recall and maintaining low query latency. In this paper, we present a vision for Policy-aware Vector Search by formalizing the FGAC policy model in vector databases as well as the enforcement problem. We compare various enforcement strategies, present preliminary findings, and identify key open challenges for future research in policy-aware vector search.
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