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To connect an AI assistant to company documents securely, retrieve only material the person asking is authorized to see, enforce that authorization in application code before sending any text to the model, and carry permissions and deletion rules through every copy of the data. Retrieval-augmented generation (RAG) can ground answers in internal sources, but it does not make a system secure by itself or guarantee correct answers.
How RAG connects an AI assistant to company documents
RAG retrieves relevant information at the time of a question and adds it to the model’s context. The assistant might use it to answer a question about a compliance report, for example, but the answer is only as safe as the path that carries the report’s contents into the model.
- Ingest and chunk documents. Extract text from approved sources and divide it into smaller passages. Attach metadata to each passage, including its source document ID, access rules, and a timestamp.
- Create embeddings and store them. Convert each chunk into a numerical representation, or embedding, that supports similarity search. Store the embedding alongside the chunk and its metadata in a vector store or another suitable index.
- Retrieve authorized passages. Convert the user’s question into a query representation, search for relevant chunks, and filter the results against the requester’s permissions before returning any text.
- Generate a grounded answer. Give the authorized passages to the language model as context. Ask it to answer from those sources and identify them so the user can check the underlying material.
This is a simplified flow: documents → chunks and metadata → embeddings and index; then question → retrieval and permission check → context → answer. The vector search is not the authorization system. The application must make that decision before the retrieved content reaches the model.
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Can RAG expose confidential company data?
Yes, if retrieval returns a chunk to someone who should not see it. A model’s instruction to “respect permissions” cannot substitute for an access-control check: once confidential text is in the model’s context, the system has already disclosed it to that model invocation.
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Enforce access before generation
Preserve access-control metadata from the source document through chunking and indexing, then apply it at retrieval time using the requesting user’s identity and permissions. In a shared or multi-tenant index, design filters and tenant boundaries so a search for one group cannot return another group’s data. OWASP’s LLM08:2025 guidance and RAG Security Cheat Sheet discuss these risks and controls.
Keep the authorization decision in deterministic application logic, not in a prompt or a model-generated judgment. Test it with users who have different access levels, including cases where similar documents belong to different teams. A search result that is relevant but unauthorized must be excluded before its text is assembled into the prompt.
Carry permissions through derived data
Chunks and embeddings are derived from the source, but they still represent its contents. If a document is deleted, access is revoked, or its permissions change, propagate that change to the corresponding chunks, embeddings, indexes, cached answers, and other derived copies. Otherwise an old vector or cached response may continue to expose information after the source system has changed.
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Log enough to investigate
Record the requesting identity, the chunks returned, and the authorization context used to permit them. Such logs can help investigate unexpected retrievals and support audits; they do not prevent unauthorized access on their own. OWASP’s RAG guidance covers retrieval logging and deletion propagation as operational safeguards, not as proof of security.
How do you prevent prompt injection in RAG?
You cannot rely on RAG to eliminate prompt injection. A malicious or compromised document can contain text that attempts to redirect the model when that text is retrieved and placed in context. Treat retrieved content as untrusted data, not as an instruction source. OWASP’s LLM01:2025 guidance specifically addresses prompt injection and the limits of RAG as a mitigation.
- Keep system instructions and application policy separate from retrieved document text.
- Use the model to summarize or answer from authorized sources, but do not let it grant access, perform privileged actions, or decide whether a user is authorized.
- Require external application checks for sensitive actions, such as accessing another system or changing a record.
- Validate outputs and test retrieved content that includes adversarial or misleading instructions.
Prompts and validation filters may reduce some failure modes, but they are not a replacement for permission checks and privilege controls enforced outside the model.
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Which RAG architecture choices affect security?
Shared or isolated storage
A shared vector store can be operationally convenient, but it raises the importance of correct tenant-aware filtering and isolation. Separate indexes or other isolation boundaries may reduce the impact of a filtering mistake, though the right design depends on the threat model and operating requirements. Whichever approach you choose, test that one tenant’s queries cannot retrieve another tenant’s material.
Vector-only or hybrid retrieval
Vector search finds passages by semantic similarity; keyword search can help find exact terms, identifiers, or phrases. Hybrid retrieval combines these approaches, but no single method is best for every corpus. Assess it against your own documents and questions, and ensure permission filtering applies to every retrieval path. André Dias Moreira Prol’s October 3, 2026 DEV Community article claims hybrid search improved accuracy by 15–25% on technical corpora, but it does not identify the study or method behind that figure, so it should not be treated as an established benchmark.
Self-hosted or managed infrastructure
Self-hosting can give an organization more control over deployment and data location, while requiring it to operate and maintain more of the system. A managed service can reduce operational work, but its data-handling configuration, access model, and location must be evaluated against the organization’s requirements. Neither choice alone establishes that document contents will never reach a public model; that depends on the specific model deployment and its configuration.
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RAG or fine-tuning for changing knowledge
RAG keeps source material in documents and retrieves it for each query, which can make changes and source tracing easier to manage. Fine-tuning changes model behavior through training and is not, by itself, a live document retrieval system. Compare the options based on how often the information changes, how updates and permissions are governed, and whether answers need traceable sources. Prol’s article also claims a nearly 70% infrastructure-cost reduction at a mid-sized firm after switching from fine-tuning to RAG, but it does not identify the firm, measurement period, or method; that figure is not a reliable general forecast.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a RAG system before rollout
- Map the data path. Identify source repositories, extraction and chunking steps, embedding and index storage, model endpoints, caches, and logs.
- Define authorization rules. Establish how user identity maps to source permissions and how those rules are preserved on chunks. Decide whether tenants need separate storage boundaries.
- Test retrieval boundaries. Use representative accounts and queries to check that relevant authorized content is returned and unauthorized content is not, including across teams or tenants.
- Exercise data changes. Revoke access, update permissions, and delete source documents; then verify the changes reach indexes, embeddings, and caches.
- Test untrusted content and outputs. Include documents containing instructions that attempt to override policy. Check that the model does not treat them as authority and that sensitive actions remain protected by external controls.
- Review logs and response procedures. Confirm that retrieval decisions can be investigated without treating logs as a substitute for preventive controls.
NIST’s draft IR 8579, dated July 31, 2025, describes a RAG-based chatbot prototype and discusses issues including prompt injection, hallucinations, data exposure, unauthorized access, local deployment, access controls, and validation filters. NIST characterizes it as a point-in-time account of technical decisions and limitations, not general implementation guidance. The voluntary NIST AI Risk Management Framework can provide broader governance context; it is not a RAG-specific recipe.
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What RAG does—and does not—establish
RAG can provide an assistant with relevant company material at query time and make it possible to show the sources used for an answer. It does not guarantee that retrieval is authorized, that a cited source supports the answer, or that the generated answer is correct. A secure design depends on access enforcement, lifecycle controls, and ongoing validation across the complete data path.
Prol’s article reports that RAG reduced hallucinations by 40–60% in enterprise studies, but does not identify those studies or their methods. Without that provenance, the number cannot be verified as a general result. Treat claims of performance or cost improvement as hypotheses to measure in your own workload rather than expected outcomes.
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