Collection-Level RBAC

Permissions are defined at the collection boundary, not per-vector. A user with the reader role on the finance_embeddings collection can query all vectors within it, while being completely denied access to the hr_embeddings collection.

• Read: Execute similarity searches and retrieve vectors
• Write: Insert, update, and delete embeddings
• Admin: Create indexes, modify schema, and manage roles

Administrators define explicit bindings between identity groups and collections. A single user can hold different roles across collections—reader on legal_docs but writer on draft_docs.

• Leverages existing identity providers (LDAP, SAML, OIDC)
• Supports role inheritance for hierarchical access models
• Enables separation of duties across data domains

Authorization checks occur at query execution, not just at connection time. When a user submits a vector search, the database engine first validates their role against the target collection's access control list before performing the similarity computation.

• Prevents bypass attacks via direct API calls
• Integrates with metadata filtering for defense-in-depth
• Logs all access decisions for audit trails

Collection-Level RBAC serves as the foundation for tenant-aware indexing in SaaS platforms. Each tenant's data resides in a dedicated collection with strict role assignments, ensuring no cross-tenant semantic leakage.

• Logical isolation without physical index duplication
• Supports namespace isolation for complex org hierarchies
• Simplifies data residency compliance per jurisdiction

The admin role at the collection level allows decentralized management. A department head can be granted full control over their team's vector collections—creating indexes, rotating keys, and assigning sub-roles—without gaining visibility into other business units.

• Reduces central IAM bottleneck
• Enables self-service data onboarding
• Maintains least privilege principle across the organization

Collection-Level RBAC often pairs with Semantic Access Control Lists for layered security. The collection role grants broad access, while a Semantic ACL applies fine-grained, concept-based restrictions within the collection.

• Example: reader on research_papers collection, but Semantic ACL blocks queries related to unreleased_product_x
• Combines coarse-grained and fine-grained authorization
• Defends against indirect semantic probing

Enforces access control at the granularity of individual vector embeddings. While Collection-Level RBAC governs entire namespaces, vector-level authorization ensures that within a permitted collection, a user can only retrieve semantically similar data they are explicitly permitted to see.

• Prevents horizontal data leakage within a single collection
• Often implemented via metadata filtering on user group tags
• Essential for multi-tenant applications storing heterogeneous sensitive data in one index

A pre- or post-query access control technique that restricts vector search results by applying Boolean constraints on associated document tags, timestamps, or user permissions. This is the primary enforcement mechanism for Collection-Level RBAC policies.

• Filters narrow the search space before or after ANN (Approximate Nearest Neighbor) retrieval
• Common operators: eq, neq, in, not in, range
• Pre-filtering ensures strict security; post-filtering may leak result counts

A multi-tenancy architecture that logically or physically partitions vector indexes to ensure strict data isolation between different organizations or business units. Collection-Level RBAC often maps directly to tenant boundaries.

• Physical isolation: Separate index instances per tenant for maximum security
• Logical isolation: Shared index with tenant ID as a mandatory filter in every query
• Trade-off: Physical isolation simplifies RBAC but increases infrastructure cost

An access control paradigm that defines permissions based on the conceptual meaning or category of data within a vector space, rather than static file paths or object IDs. Extends traditional ACLs into the semantic domain.

• Permissions are tied to embedding clusters or concept labels
• A user with access to 'financial reports' may query any vector semantically classified as financial
• Requires a classification model or ontology to map vectors to semantic categories

A unified access control layer that simultaneously enforces permissions across both dense vector embeddings and sparse keyword metadata during a single retrieval operation. Critical when Collection-Level RBAC must apply to hybrid search pipelines.

• Merges permission checks from vector stores and inverted indexes
• Ensures a user cannot bypass semantic restrictions by falling back to keyword search
• Requires a federated authorization service that both retrieval paths consult

A protective network layer that inspects and sanitizes vector queries and responses to prevent adversarial inputs, extraction attacks, and unauthorized semantic access. Sits between the application and the vector database.

• Validates that query vectors conform to expected dimensionality and distribution
• Blocks queries attempting to probe collection boundaries via similarity threshold gating
• Logs all access attempts for audit trails independent of the database