Building the security layer AI systems are missing

As AI agents gained the ability to query vector databases, a structural gap opened up: vector search has no native concept of who is asking or what they are permitted to see. A retrieval pipeline that returns top-k nearest neighbors does not consult your IAM system, your RBAC policies, or the sensitivity classification on a document. The result is that confidential, internal, or personally identifiable content can flow directly into an LLM prompt with no access check performed.

Gateco sits between AI agents and the knowledge stores they query. Every retrieval request is evaluated against policies that combine RBAC, ABAC, and REBAC — the same access control models that enterprise IAM teams already use for human access. Policies are enforced at the chunk level, not just the document level, so a principal who can see a document's title cannot necessarily see its body. The policy engine is deny-by-default: if no active policy explicitly grants access, the retrieval is blocked and the decision is recorded in the audit log.

The audit trail is the other half of the equation. Knowing that access was denied is not enough — you need to know which principal asked, which resource they asked for, which policy made the decision, and when. Gateco records every retrieval event with that full context, giving security and compliance teams the visibility they need to understand how AI systems interact with sensitive data at scale.