Multi-Tenant LLM Authorization for SaaS: Preventing Cross-Tenant Data Leakage

SaaS platforms are racing to embed LLM features — AI assistants, intelligent search, document Q&A. The architecture challenge is that most SaaS platforms share infrastructure across tenants for cost efficiency, but AI features create new paths for cross-tenant data leakage. A shared vector database that ingests documents from multiple tenants can return Tenant A's data to Tenant B if authorization isn't enforced at the retrieval layer.

The naive solution is separate vector databases per tenant. This works at small scale but destroys the cost efficiency of shared infrastructure and makes operations significantly more complex. A better approach is shared infrastructure with per-tenant policy enforcement — which is exactly what Gateco provides.

Tenant isolation in Gateco uses ABAC policies that match on resource.labels (containing the tenant ID) and principal.attributes.tenant_id. A policy rule like "principal.attributes.tenant_id equals resource.labels.tenant_id" ensures that retrieval results only include vectors belonging to the requesting tenant. This is enforced at the policy engine level, not the application level, so every AI feature that queries the shared vector database automatically respects tenant boundaries.

Per-tenant policies go beyond simple isolation. SaaS platforms often have complex authorization models: workspace admins who see everything in their tenant, project-scoped access for team members, read-only access for external collaborators. Gateco's ABAC conditions can express these hierarchies: principal.roles contains "workspace_admin" AND principal.attributes.tenant_id equals resource.labels.tenant_id, or more granular rules matching project IDs and collaboration permissions.

The operational advantage is observability. Gateco's audit trail records every retrieval with the tenant context, making it straightforward to detect and investigate potential cross-tenant access. SaaS platforms can export audit data per tenant for enterprise customers who require their own compliance evidence, or stream events to a SIEM for real-time monitoring of authorization anomalies.

For SaaS platforms evaluating Gateco: start with the free tier to validate tenant isolation on your existing vector infrastructure. The typical integration path is to add tenant metadata to your ingestion pipeline (resource.labels.tenant_id), define per-tenant policies, and route all AI retrieval requests through Gateco's execute endpoint. The Python and TypeScript SDKs make this a minimal code change — usually under 20 lines in your existing retrieval service.