Most enterprise AI programs that reach production have some form of evaluation suite. They score accuracy, check for regressions, and gate releases when quality drops below a threshold. Fewer have anything resembling red teaming: a structured, adversarial effort to make the system fail on purpose, before a customer, regulator, or attacker does it for you. That gap is worth closing before it becomes the reason an AI incident turns into a public one.
Evaluation and red teaming answer different questions. An eval suite asks whether the model does the job well on the inputs you expect. Red teaming asks what happens on the inputs you didn’t expect, and whether someone with bad intent can steer the system somewhere you never intended it to go. A support agent that summarizes tickets accurately in testing can still be walked, through a crafted sequence of messages, into revealing another customer’s data, approving a refund it has no authority to approve, or ignoring the safety instructions in its own system prompt. None of that shows up in an accuracy score. It shows up when someone tries to break the thing on purpose.
The practical version of this doesn’t require a dedicated security research team, though larger enterprises increasingly build one. A workable starting point is a recurring exercise where a small, rotating group, ideally including people who did not build the system, spends focused time trying to make it misbehave: prompt injection through tool outputs and retrieved documents, jailbreaks that bypass system instructions, attempts to extract training data or system prompts, and probes that test whether the agent will take actions outside its intended scope when asked persistently enough. The findings get logged the same way a security vulnerability would: severity, reproduction steps, and an owner responsible for a fix.
The highest-value target for this exercise is usually the boundary between the model and anything it can act on. Chatbots that only generate text have a limited blast radius. Agents that can call APIs, write to databases, send emails, or trigger downstream workflows do not. If an adversarial prompt can convince an agent to invoke a tool it shouldn’t, the consequence is not a bad response, it’s an unauthorized action. Red teaming that focuses only on offensive language or factual accuracy misses this entirely, which is why the exercise needs to be scoped around what the system can actually do, not just what it says.
Timing matters as much as method. Red teaming works best before a system reaches general availability, and again after any material change: a new tool gets added to an agent’s toolkit, a model gets swapped for a newer version, or the system prompt gets rewritten. Enterprises that treat red teaming as a one-time pre-launch checkbox tend to rediscover the same vulnerability classes every time the underlying model changes, because a fix that worked against one model’s failure modes doesn’t automatically transfer to the next one.
None of this needs to slow delivery down. The teams that do it well fold it into the same release gate as the evaluation suite: an AI change doesn’t ship until it clears both bars, quality and adversarial resilience. Treating red teaming as a parallel discipline rather than an afterthought is what separates AI programs that discover their failure modes internally from the ones that discover them in an incident report.
