Architecting Zero Trust for Enterprise AI Pipelines

Short summary

The article argues that Zero Trust security must extend into the AI pipeline itself, not stop at network boundaries. It identifies key attack surfaces across data ingestion, embedding, retrieval, and generation stages, and prescribes five practical controls: identity-aware retrieval with RBAC on vector layers, data sanitization before embedding, context window authorization, output guardrails, and pipeline component isolation. LLMs don't inherently respect user permissions, making in-pipeline authorization essential.

• •LLMs surface information by semantic similarity, not user authorization — traditional perimeter security is insufficient
• •Five key controls: identity-aware retrieval, pre-embedding sanitization, context window authorization, output guardrails, and component isolation
• •Vector database chunks must carry access-control metadata so retrieval filters by authenticated identity before generation

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