What is AI red teaming?
AI red teaming is a deliberate, adversarial test of an AI system in which a team plays the attacker and tries to make the system misbehave. The term comes from military and cybersecurity practice, where a red team probes defenses so defenders can fix them. Applied to generative AI, the red team tries jailbreaks, prompt injection through documents and web pages the system reads, attempts to extract the system prompt or other users’ data, and requests that push the system to act outside its authority. Each finding is written up with the exact input, the result, and a severity rating.
Red teaming differs from ordinary testing in intent: functional tests check that the system does what it should on expected input, while red teaming checks what it does when someone wants it to go wrong.
How AI red teaming works
A red team exercise starts with a threat model: who might attack the system, what they would gain, and what the worst outcomes would be. The team then works through categories of attack, combining manual creativity with automated tools that generate thousands of adversarial prompt variants. Findings go to the engineering team, who fix guardrails, tighten tool permissions, or change the design, and the red team retests. Mature programs repeat the exercise at every major change and run a lighter automated version continuously in production.
AI red teaming in practice
A regional bank prepares to launch an assistant that answers customer questions about accounts and can initiate a card freeze. Before launch, a red team spends two weeks on it. They get the assistant to reveal its system prompt within an hour, find that a crafted question returns another customer’s branch visit history because a retrieval query lacked an account filter, and show that a long roleplay conversation produces a card freeze on an account the user does not own. All three are fixed before any customer sees the system. In Kirk Tech Solutions engagements, a red team pass of this kind is part of the acceptance criteria for any agent that can read customer data or change a record.
What to watch for
- Red team the whole system, including retrieval, tools, and connected applications, since most serious findings are in the integration rather than the model.
- Include people who know the business, because an attacker who understands your refund policy or approval thresholds finds different holes than a security generalist.
- Treat vendor red teaming of a base model as a starting point only, since it says nothing about your prompts, data, or tools.
- Track findings to closure and retest, as a report that is filed and forgotten changes nothing.