This checklist consolidates the RAG-specific security controls discussed across this cluster into a single working reference, organized by pipeline stage so a review can move through it systematically.
Retrieval access control
Confirm candidate documents are filtered by the requesting user's actual permissions before ranking by relevance, not after. Confirm the retrieval system carries forward the same access control model already enforced by the original document sources, kept synchronized as permissions change. Confirm security-relevant metadata fields used for filtering, department, classification, tenant identifier, are enforced as mandatory, structural filters rather than optional application-level parameters.
Ingestion pipeline
Confirm content review, pattern-based scanning, structural checks for hidden content, and provenance-based trust weighting, happens before content enters the index, not after. Confirm write access to sources feeding retrieval is restricted to a small, trusted group with changes reviewed. Confirm metadata assignment at ingestion is tested with the same rigor as retrieval filtering logic, since a metadata error at ingestion propagates to every future retrieval of that content.
Vector database
Confirm the vector database has authentication and network isolation matching the standard applied to other sensitive data stores. Confirm tenant isolation is structural, using separate collections or a mandatory, enforced tenant identifier, rather than an optional application-level filter. Confirm embeddings and their associated metadata are treated as sensitive, encrypted, and access-controlled, not assumed to be safely anonymous because they are numeric.
Chunking and context handling
Confirm chunking preserves or correctly propagates access metadata to every resulting chunk. Confirm chunking does not strip protective framing, caveats, or exceptions, from content in ways that change its meaning when retrieved in isolation. Confirm context included in any given request is limited to what the task actually needs, not entire documents included by default.
Data minimization and privacy
Confirm personal and sensitive data included in retrieved content is limited to what a specific task requires. Confirm sensitive values not needed in plain text are redacted or tokenized before entering a model's context. Confirm retention limits are applied specifically to embeddings, caches, and logs, not left to a single blanket policy or indefinite default retention.
Data subject and deletion handling
Confirm deletion of a source document propagates to its corresponding embedding and any cached derivatives, not only the primary document store. Confirm there is a practical way to search logs and traces for content related to a specific individual, in support of data subject requests. Confirm any use of personal data in fine-tuning has been evaluated for the practical limitations of future removal.
Multi-tenant and cross-boundary risk
Confirm strict, structurally enforced isolation for any multi-tenant or multi-customer retrieval index. Confirm cross-document combination risk has been considered explicitly, not only per-document access control. Confirm testing includes deliberate cross-tenant and cross-role retrieval attempts, not only generic functional queries.
Domain-specific compliance considerations
Confirm legal, healthcare, or financial content categories, if present, have distinct, appropriately stricter access scoping beyond general role- based permissions, matching the specific regulatory or privilege obligations that apply to that content. Confirm third-party model provider arrangements have been reviewed against the specific compliance requirements relevant to any sensitive data category involved.
Monitoring and incident readiness
Confirm retrieval activity is logged with enough detail to trace an unusual response back to the specific content that likely produced it. Confirm there is a plan for investigating a suspected cross-tenant or cross-boundary exposure, including how to determine scope and communicate with affected parties if the system is customer-facing.
Using this checklist proportionally
Not every RAG deployment needs equal depth on every item. An internal, low-sensitivity knowledge base can move through this checklist relatively quickly. A customer-facing, multi-tenant system touching legal, healthcare, or financial content deserves a thorough pass through every category, since the consequence of a gap scales with both exposure and data sensitivity.
Where TELEON fits
Several items here, tool authorization following retrieval, audit logging of retrieval activity, and tokenization of sensitive values, map to what TELEON's runtime policy enforcement, audit trail, and privacy vault tokenization provide when deployed through the gateway or supported middleware. The pipeline-level items, access-filtered retrieval, ingestion review, vector database hardening, and chunking design, are architecture decisions that have to be built directly into the retrieval system.
The checklist in short form
- Retrieval access control filtered before ranking, using the source
system's own permission model.
- Ingestion review and restricted write access for every feeding source.
- Vector database authentication, isolation, and embedding protection.
- Chunking that preserves metadata and protective context.
- Data minimization and sensitive value tokenization.
- Deletion and data subject request paths reaching embeddings and caches.
- Structural multi-tenant isolation and combination-risk review.
- Domain-specific compliance scoping for legal, healthcare, or financial
content.
- Monitoring and incident readiness for retrieval-specific exposure.
Working through these categories systematically, rather than relying on a general sense that "the RAG system looks fine," is what catches the specific, recurring gaps, unfiltered retrieval, unreviewed ingestion, under-protected vector storage, that make up most real RAG security incidents.