Inferensys

Glossary

Identity Propagation

The secure transmission of the end-user's authenticated identity context through the RAG pipeline layers to ensure the retrieval engine applies the correct user-specific permissions.
Developer working on RAG retrieval system, document chunks visible on screen, technical workspace with code editor.
RAG PERMISSIONING

What is Identity Propagation?

The secure transmission of the end-user's authenticated identity context through the RAG pipeline layers to ensure the retrieval engine applies the correct user-specific permissions.

Identity Propagation is the secure conveyance of an end-user's authenticated identity context across every tier of a Retrieval-Augmented Generation (RAG) pipeline, ensuring that the vector database and retrieval engine enforce user-specific permissions rather than relying on a generic service account. This mechanism binds the original requester's security principal to the data retrieval operation, preventing privilege escalation and cross-user data leakage.

Effective propagation requires passing cryptographically signed tokens or headers from the application layer through to the Policy Enforcement Point (PEP). This allows downstream components like the Policy Decision Point (PDP) to evaluate Attribute-Based Access Control (ABAC) policies against the actual user's attributes, enabling fine-grained Chunk-Level Authorization and Metadata Filtering that respects the principle of Least Privilege Retrieval.

SECURE CONTEXT TRANSMISSION

Key Features of Identity Propagation

The core mechanisms that ensure an end-user's authenticated identity context is securely transmitted through every layer of the RAG pipeline, from the initial query to the final document retrieval.

01

Context Token Injection

The process of embedding a cryptographically signed user identity token into the retrieval request header. This token is propagated through microservices without requiring the user to re-authenticate at each hop.

  • JWT Assertions: Uses JSON Web Tokens containing user ID, roles, and attributes.
  • Header Forwarding: Proxies and API gateways strip external auth and inject internal identity headers.
  • Mutual TLS (mTLS): Establishes certificate-based identity verification between services in the pipeline.
02

Downstream Impersonation

A delegation mechanism where a middleware service, after verifying the original user's identity, obtains a delegated access token to act on behalf of that user when querying the vector database.

  • OAuth2 On-Behalf-Of Flow: Exchanges a user token for a new token scoped to the downstream retrieval service.
  • Service Account Mapping: Maps the user's abstract permissions to a technical service account for database access.
  • Prevents Privilege Escalation: Ensures the retrieval engine does not use its own super-user credentials to fetch data.
03

Metadata Filter Propagation

The mechanism by which user attributes from the identity token are translated into metadata filters applied directly to the vector database query. This ensures the semantic search only operates over authorized document partitions.

  • Attribute Mapping: Transforms LDAP groups or OIDC claims into vector store metadata key-value pairs.
  • Dynamic Query Scoping: Injects user.department = 'finance' into the retrieval query automatically.
  • Pre-Retrieval Enforcement: Filters the search space before the similarity calculation, preventing data leakage.
04

Session-Level Trust Chain

Maintains a continuous, verifiable chain of trust from the end-user's initial authentication through every asynchronous event in the RAG pipeline, including re-ranking and generation.

  • Trace Context (W3C): Propagates traceparent headers to correlate identity with telemetry across services.
  • Immutable Audit Trail: Links every retrieval log entry back to the originating user session.
  • Continuous Authorization: Re-validates the user's session status at the Policy Decision Point (PDP) before final output generation.
IDENTITY PROPAGATION

Frequently Asked Questions

Common questions about securely transmitting end-user identity context through retrieval-augmented generation pipelines to enforce user-specific document permissions.

Identity propagation is the secure transmission of the authenticated end-user's identity context—including user ID, group memberships, and attributes—from the application layer through the retrieval pipeline to the vector database and policy decision point. This mechanism ensures that when a user submits a query, the retrieval engine executes the semantic search with the user's specific permissions rather than a service account's elevated privileges. Without proper propagation, a RAG system operating under a single system credential would bypass all user-level access controls, potentially surfacing confidential documents to unauthorized individuals. The propagation chain typically traverses multiple trust boundaries: the client application, API gateway, orchestration layer, embedding service, and ultimately the vector store's authorization filter.

AUTHORIZATION ARCHITECTURE COMPARISON

Identity Propagation vs. Service Account Retrieval

A technical comparison of the two primary architectural patterns for enforcing user-specific permissions in RAG pipelines: forwarding the end-user's identity context versus using a static service account with post-retrieval filtering.

FeatureIdentity PropagationService Account RetrievalHybrid Model

Authorization Granularity

User-specific, document-level

Coarse-grained, role-based

User-specific with fallback

End-User Identity in Audit Logs

Requires Post-Retrieval Filtering

Latency Impact

< 50 ms overhead

100-300 ms overhead

50-150 ms overhead

Risk of Privilege Escalation

Low

High

Medium

Vector Store ACL Synchronization Required

Supports Context-Aware Access

Compatible with Legacy Data Sources

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.