Everyone has heard of red teaming by now. In the AI world it means something specific: a team of testers attacks your AI assistant the way a real attacker would, and reports what worked. Not in theory. Not from a checklist. They sit down and actually try.
This post is a look at what actually happens in one of those tests, in plain language. If you are planning to add AI to your business, this is the conversation you will have sooner or later.
The test starts with a written scope, the same as any security test: what the assistant is, what it is connected to, and what may and may not be touched. Nothing runs against live customer data without agreement.
Then the trying starts. Hidden instructions in chat messages and in documents the assistant might read. Messages pretending to be the owner, asking for refunds or account changes. Attempts to reach the systems connected to the assistant: orders, customer records, payment tools. Questions chained together, each one steering a little further. Every attempt is logged, with what the assistant did in response.
What usually fails is not the AI model. It is the setup around it. The assistant has access it does not need. The systems around it trust its output too much. Nobody put a rule layer between the assistant and anything important. Those are the findings a red team test surfaces, and the fixes are usually configuration, not rebuilding.
Fix first: give the assistant the least access it can do its job with, treat anything a customer sends as data rather than instructions, and test before launch. If it is already live, test now, before the wrong customer finds out what it will do when pushed.
AI red teaming is the same idea as every other security test: find out what breaks in a controlled way, so you are not finding out in public.