Definition
Permission-aware retrieval
Permission-aware retrieval is a retrieval architecture in which every candidate result is evaluated against the requesting user's real identity and access policies at query time, before any content reaches the language model. The model cannot leak what it never received.
The term names the missing property in most RAG systems: retrieval that knows who is asking. Vector search alone optimizes for similarity. IAM alone authorizes the application, not the person. Permission-aware retrieval joins the two, per query, per chunk.
The late-binding architecture
Authorization binds at retrieval time, not at indexing time. That single choice is what makes revocation instant and policy changes re-index-free.
1. Permission-blind recall
The query runs against the vector database normally (vector, keyword, or hybrid search) and returns the top candidate chunks by relevance. Recall stays complete: nothing relevant is missed because of pre-filtering.
2. Per-chunk policy evaluation
Each candidate is evaluated against the requesting principal: their groups and attributes synced from the identity provider, the resource's classification and metadata, and the active policies (RBAC, ABAC, or relationship-based). The default is deny: a chunk with no matching allow policy is dropped.
3. Secure resolution and audit
Only the authorized subset is returned to the application or synthesized into an answer. Every decision is recorded: principal, resource, policy, verdict, and timestamp. Denials are visible as metadata, never as content.
Two users asking the identical question get different answers, each complete within their clearance. The pattern is described in depth in pre-retrieval vs post-retrieval authorization and demonstrated end to end in the recorded product demo.
What it deliberately does not solve
Permission-aware retrieval is one layer of an AI security program, not the program. Claiming otherwise would be wrong, so here is the boundary:
Prompt injection and jailbreaks
Permission-aware retrieval bounds what the model can see, which limits the blast radius of an injected prompt, but detecting and neutralizing adversarial instructions is a separate control.
Model security and red-teaming
Training-data extraction, fine-tuning integrity, and model supply chain are model-layer concerns, not retrieval-layer ones.
Output moderation and DLP on non-retrieval channels
If users paste sensitive data into prompts or the model generates from its own weights, retrieval enforcement never sees it.
Network and infrastructure authorization
Service-to-service authz, API gateways, and workload identity remain the domain of IAM and policy engines such as OPA.
Gateco implements permission-aware retrieval
Identity sync from your IDP, per-chunk policy evaluation, deny-by-default, and a full audit trail across 12 vector databases. When it is not the right fit, the RAG authorization guide says so honestly.