Security testing for language systems

A language interface is an attack surface. Governance that lives only in a policy document will not catch an injection, a leak, or a tool call that should never have been possible.

Enterprise AI systems accept untrusted language and then touch trusted tools and data. That combination deserves the same seriousness as any other production boundary. A prompt is not a soft input. It is a place where instruction, content, and sometimes attack travel together.

Testing has to be specific to the system you built. Can a user redirect the assistant into revealing data it was allowed to see for a different purpose? Can retrieved content carry an instruction the model will follow? Can a tool be convinced to act outside the role of the person who asked? Data-leakage tests belong next to those questions, using realistic records and a clear definition of what must never leave the boundary.

Guardrails and governance are the design response, not a paragraph in a principles deck. A guardrail that is not tied to identity, retrieval scope, and tool permissions is a filter hoping for the best. A governance framework that names an owner, a risk rating, and a review cadence gives the test findings somewhere to land.

Regulatory readiness sits downstream of this work. You cannot claim a control you have not implemented, and you should not implement a control you have not tried to break. Security testing is how an AI system becomes something a risk committee can reason about without taking the demo on faith.