Why row-level security and application-layer RBAC are necessary but not sufficient for multi-tenant clinical AI, and the isolation architecture that health system security teams actually audit.
Standard SaaS controls protect structured data, not LLM context windows, embeddings, or inference logs.
Healthcare AI introduces three new PHI exposure surfaces that can leak data across tenants without proper isolation.
Shared retrieval indexes or cached contexts can put one tenant's PHI into another tenant's prompt. Standard access logs do not catch it because no database query crossed tenants.
Without tenant metadata filtering at query time, semantic search returns nearest-neighbor chunks from any tenant whose data was embedded into the index.
Shared logging infrastructure without tenant partitioning returns PHI from multiple tenants on unscoped queries. The same telemetry stack that worked for non-PHI SaaS becomes a HIPAA liability.
Per-tenant vector indexes (or strict metadata filters), with audit logging of every retrieval context. Cross-tenant retrieval becomes impossible by architecture, not by configuration.
Vector queries enforce tenant filters at the database layer. Application bugs cannot bypass the filter; the database refuses to return cross-tenant embeddings.
Inference traces partitioned by tenant. PHI scrubbed before storage. Per-tenant fine-tuning or differential privacy to prevent model memorization of one tenant's data leaking into another tenant's queries.
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