AI agent access control: a practical guide

This post details practical strategies for implementing AI agent access control, focusing on enforcing permissions at the retrieval layer within RAG pipelines. It introduces metadata filtering, permission-aware retrieval, and layered policy enforcement as key patterns. The post elaborates on document-level, row-level, and field-level access control, advocating for ABAC and ReBAC models over static RBAC for dynamic agent authorization. It stresses that access controls must live at the data layer, upstream of the LLM, to prevent prompt injection bypasses. The core contribution is a practical guide to preventing data leaks by ensuring agents only access data they are authorized for, based on the user, task, and workflow context, rather than just their authenticated identity.