AI-SPM vs CSPM

Cloud Security Posture Management grew up answering one question well: is this account configured the way it should be. Open S3 buckets, over-broad IAM roles, unencrypted volumes, a security group someone left wide open at 2am. It reads the control plane of your cloud and tells you where the configuration drifted from policy. For infrastructure, that is most of the battle.

An LLM application breaks that model quietly. The bucket is private, the IAM role is scoped, the volume is encrypted - CSPM is happy - and the chatbot is still handing one customer's support history to another because a retrieval filter runs after the rank instead of inside the query. Nothing in the cloud configuration is wrong. The risk lives one layer up, in how the model and its retrieval pipeline behave at request time, and CSPM was never built to look there.

So a clean CSPM report on an AI workload is not reassurance. It is a statement about the plumbing, not about the water.

AI-SPM picks up exactly where CSPM stops. It inventories the AI surface CSPM does not model - LLM endpoints, the agents calling MCP servers, the RAG pipelines, the vector stores, the gateways - and then it tests behaviour rather than configuration. It runs adversarial probes against the live endpoint, scores tool permission risk on each agent, and watches what crosses the wire.

The honest framing is that these are not competitors. Most teams we work with run both: CSPM keeps the account configured correctly, AI-SPM keeps the AI running inside it from leaking, over-acting, or failing an audit. The overlap is small and the gap between them is exactly where 2026's AI incidents are landing.

AI-SPM covers the AI-specific controls CSPM leaves open: NIST AI 600-1 MAP and MEASURE functions, EU AI Act Articles 10 and 15, OWASP LLM Top 10, and OWASP Agentic Top 10. CSPM evidence still answers the cloud-configuration questions in ISO 27001 and SOC 2; the two evidence sets sit side by side.