AI answers
What is an AI red team?
Red teaming is the only reliable way to find out what your AI system does when someone is actively trying to make it misbehave.
How it differs from ordinary testing
Normal testing asks whether the system does the right thing when used correctly. Red teaming assumes bad faith. The tester is trying to get the system to leak another customer's data, ignore its instructions, produce something reputationally damaging, or call a tool it should never have called. Because model behaviour is probabilistic rather than deterministic, this cannot be replaced by unit tests. You are sampling a behaviour space, not verifying a function.
What gets tested
- Prompt injection: instructions hidden in a document, a web page or an email that the agent reads and follows. This is the highest-severity category for any system that ingests untrusted content.
- Data leakage: getting the system to reveal its system prompt, another user's records, or documents outside the requester's access level.
- Tool and action misuse: persuading an agent with real permissions to send, delete, purchase or modify something it should not.
- Refusal failures: harmful or off-scope requests the system answers anyway, often after being reframed.
- Bias and fairness: differing quality of output across names, dialects, genders or regions, which matters most in hiring, lending and healthcare contexts.
- Fabrication under pressure: whether the system invents a plausible answer when the retrieval layer returns nothing.
Who should run it
Not the people who built it, because they unconsciously test the paths they designed. It can be a specialist external team, an internal group from another part of the business, or a mixed group where domain experts supply realistic attacks and engineers supply technical ones. The domain expert contribution matters more than most teams expect: a claims handler knows which phrasing gets a policy exception approved, and that is exactly the attack you will not think of.
What a finding looks like
A usable finding contains four things: the exact input, the actual output, why it matters in business terms, and a severity. Vague reports such as "the model sometimes hallucinates" cannot be fixed or retested. The reproducible input is the essential part, because every fixed finding should become a permanent test case that runs again on the next model update.
When it is worth doing
Any system that reads untrusted input, holds permissions to act, touches personal or regulated data, or speaks to customers unsupervised. A read-only internal tool over public documents does not need a formal exercise. A customer-facing agent with database access needs one before launch and after any significant change, because a model provider updating a model underneath you can undo a mitigation you never revisited.
Red teaming is one part of the pre-launch checklist we run in an AI audit, alongside data mapping and evaluation design.
Frequently asked questions
What is an AI red team and what does it do?
It is a group that deliberately attacks an AI system before real users do, testing prompt injection, data leakage, jailbreaks, tool misuse, bias and fabrication. Each issue is documented with the exact input that triggered it, the output produced, the business impact and a severity, so engineering can fix it and keep it as a permanent test.
How is red teaming different from normal QA?
QA checks the system does the right thing when used correctly. Red teaming assumes the user is hostile and creative. Because model outputs are probabilistic rather than deterministic, you are sampling a behaviour space rather than verifying a function, which is why unit tests alone never surface these failures.
When does an AI system need red teaming?
When it reads untrusted input, holds permissions to act, touches personal or regulated data, or talks to customers without supervision. A read-only internal tool over public documents generally does not. Anything customer-facing with database access should be tested before launch and again after significant changes, including model provider updates.
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