AI-SPM vs DSPM

DSPM does something genuinely useful. It walks your stores, classifies what is sensitive, traces lineage, and flags the database nobody remembered was holding production PII. For the data-at-rest problem it is the right tool.

RAG breaks the at-rest assumption. The moment a pipeline embeds a document and drops it into a vector store, the data exists in a new form DSPM was not built to reason about - a dense vector that can be inverted back toward the original text, sitting in an index that may or may not isolate one tenant from another. The sensitive document is still catalogued correctly in its source store. The copy that leaks is the embedding, and the leak happens at retrieval time when a similarity query crosses a namespace it should not.

That is the recurring finding: DSPM says the data is governed, and a cross-tenant retrieval test says it is walking out through the assistant anyway.

AI-SPM treats the AI data flow as the thing to test, not just the store. It registers each RAG system as a typed asset - embedding model plus vector store plus the data sources feeding it - and runs probes that DSPM has no equivalent for: corpus tainting, retrieval-boundary leakage, embedding-space adversarial inputs, and tenant-isolation defects, seeded with canary documents so a leak is verifiable rather than theoretical.

None of this makes DSPM redundant. DSPM still owns the question of which stores hold what. AI-SPM owns the question of whether the AI reading those stores respects the boundary once the data is in motion. Run them together and the handoff is clean: DSPM classifies, AI-SPM proves the classification survives contact with the model.

AI-SPM evidence maps to EU AI Act Article 10 (data governance), OWASP LLM01 (indirect injection) and LLM06 (sensitive disclosure), and NIST AI 600-1 MEASURE-2.7. DSPM lineage evidence continues to answer the data-residency and classification questions in GDPR and ISO 27001. The pair covers data at rest and data in AI motion.