Source: https://daphnislabs.com/blog/rag-permissions-before-vector-search Last updated: 2026-08-25T09:00:00+05:30 [All articles](https://daphnislabs.com/blog) # Design RAG permissions before you tune vector search 25 August 2026[AI Engineering](https://daphnislabs.com/blog/category/ai-engineering) ![OpenAI logo on a phone beside an open laptop on a desk.](https://daphnislabs.com/images/blog/photos/rag-permissions-before-vector-search.webp) Imagine a support assistant that answers a question accurately but cites another customer's contract. Better ranking will not fix that failure. A retrieval system needs an access model that travels with the documents, their chunks and the requests that search them. Start there before comparing embedding models. ## Give each source an owner Build an inventory containing the source system, document identifier, tenant, access groups, revision and deletion policy. Decide which system is authoritative for each field. If a shared drive controls membership, copying its permissions once during ingestion leaves a gap when a person changes teams. Define how changes reach the index and what the assistant does while an update is pending. Microsoft's [RAG overview](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview) describes retrieval as the step that supplies grounding material to a language model. In your design, draw the permission check before that material enters the model context. Asking the model to ignore a confidential passage is not an access boundary. ![Two document collections feed a permission filter; only the authorised collection continues to the answer and its source citation.](https://daphnislabs.com/images/blog/editorial/rag-permissions-before-vector-search.svg) ## Test with identities, not just questions Use a small collection of synthetic documents with deliberately different access rules. Ask the same question as a support agent, a manager and a user from another tenant. Extend the fixture with [database tenant-isolation tests](https://daphnislabs.com/blog/postgres-tenant-isolation-tests) when retrieval depends on a shared application database. Verify the returned passages and citations as well as the final answer. Include renamed documents, removed group membership and a deleted source. A correct refusal is a successful result when no authorised evidence exists. ## Make the answer traceable Keep a stable connection between each passage and the source revision it came from. A citation should lead a permitted reader to useful evidence, not merely to a document title. If the source is withdrawn, stop retrieving it and decide how [previously cached answers expire](https://daphnislabs.com/blog/api-caching-without-stale-account-data) . Apply the same access rules to logs and evaluation exports, which can otherwise become another copy of restricted text. For a [RAG development project](https://daphnislabs.com/services/rag-development) , the initial deliverable should include this access map alongside retrieval examples. The accompanying [data pipeline](https://daphnislabs.com/services/ai-data-pipelines-vector-db) needs a repair path for missed updates, so the team can reconcile the index against the source instead of relying on a successful ingestion message. ## Related articles [View all blogs](https://daphnislabs.com/blog) AI Engineering ### [Kimi K3.1 Pro API: Pricing, Context Limits and a Multi-File Refactor Test](https://daphnislabs.com/blog/kimi-k31-pro-api-pricing-context-multi-file-refactor-test) Separate Kimi's API and coding-plan limits, budget cache writes correctly, and evaluate a repository refactor against behavior that must survive the change. AI Engineering ### [Mercury 3 API: Launch Pricing, Context Limits and a Support-Agent Latency Test](https://daphnislabs.com/blog/mercury-3-api-pricing-context-support-agent-latency-test) Budget beyond Mercury 3's launch discount, check its streaming and reasoning defaults, and measure how quickly a support agent produces a correct, usable answer. AI Engineering ### [Muse Spark Open: API Pricing, Context Limits and a Self-Hosting Evaluation](https://daphnislabs.com/blog/muse-spark-open-api-pricing-context-self-hosting-evaluation) Separate the model's context from each provider's limits, calculate a coding-pilot budget, and test a mobile layout repair before committing to self-hosted hardware.