Most AI security programs are built to keep attackers out of the infrastructure around a model. AI red teaming flips that and asks a harder question: what happens when the attacker talks to the model itself? It is the structured practice of probing an AI system the way a real adversary would, finding the inputs that make it leak data, ignore its guardrails, or take an action it should never take. This post is for the security leaders who now own that risk: what AI red teaming is, how it differs from the red team exercise you already run, which techniques and tools actually find AI security vulnerabilities, and how to fold red teaming into a program that holds up under regulators like the EU AI Act. If your AI adoption is moving faster than your testing, this is the gap to close.
What is AI red teaming, and why does it matter?
AI red teaming is a structured form of adversarial testing where a team deliberately attacks an AI system to surface vulnerabilities in AI systems before a real adversary does. Red teaming originated in military planning, where one group played the enemy to pressure-test a strategy. The idea carried into cybersecurity, and now into AI. The difference with an AI red team is what you are attacking: not just code and infrastructure, but model behavior itself, the outputs an AI model produces when someone feeds it adversarial input.
It matters because AI systems fail in ways traditional security testing was never built to catch. A pen tester looks for a flaw in code that behaves the same way every time. An AI red teamer is dealing with a probabilistic system where the same prompt can produce different responses across runs, and where a cleverly worded request, not a code exploit, is the attack. That's why red teaming has become the baseline practice for securing AI systems rather than a nice-to-have. You cannot patch what you have not provoked into misbehaving.
The stakes climb as AI moves into agentic territory. When an AI system can call tools, touch data, and take actions, a single successful adversarial input stops being a content problem and becomes an operational one. AI red teaming helps you find those paths, where a prompt convinces an agent to exfiltrate grounding data or invoke an internal API, while you can still do something about it.
How is AI red teaming different from traditional red teaming?
Traditional red teaming focuses on the cyber kill chain: phishing, lateral movement, privilege escalation, the well-mapped territory of attacks against networks and applications. AI red teaming probes a different surface. The adversary is trying to make the model itself misbehave, and the "payload" is often just persuasive natural language. Where traditional red team testing has signatures and known techniques, AI red teaming deals with novel risks that have no fixed shape.
There's a second difference that trips up teams new to this. A traditional red team exercise tests one risk surface, security. An AI red teaming exercise tests two at once: security vulnerabilities like prompt injection and data exfiltration, and responsible AI harms like bias, toxicity, and manipulation. The same probing session that checks whether an attacker can jailbreak your model also checks whether the model says something it shouldn't to an ordinary user. That dual mandate is why AI red teaming involves people who understand both offensive security and how AI models actually fail.
The probabilistic nature changes the method too. Because outputs vary, you cannot run one attack and call it tested. Effective AI red teaming runs many variations, scores the responses, and looks at success rates rather than pass/fail. It is closer to statistical sampling than to the deterministic confirm-and-move-on rhythm of traditional security work.
What are common AI red teaming techniques?
The workhorse is prompt-based adversarial attacks. Direct injection, where an attacker types something that overrides the system's instructions, and indirect injection, where the malicious instructions hide inside content the model later reads, like a webpage or document. Red teamers also use multi-turn techniques, escalating gradually across a conversation so the model is coaxed past its guardrails one step at a time rather than in a single obvious ask.
Then there's the converter toolkit: rephrasing a blocked prompt, encoding it, translating it, or stacking those transformations so an adversarial input slips past safety filters that would catch the plain version. These red teaming techniques exist because AI safety alignment often catches the obvious phrasing but not the disguised one. A red team can simulate exactly that gap, which is the whole point, finding what the model's defenses miss before someone hostile does.
For agentic systems, the techniques extend to tool abuse and workflow manipulation: getting an AI agent to call a function it shouldn't, chain actions in an unsafe order, or treat attacker-supplied text as a trusted command. As AI agents take on more autonomy, this category of red teaming activities grows fastest, because the blast radius of a successful attack grows with it. Teams building in this space should pair red teaming with hardened agent design from the start, which is the throughline of our look at how employees are already managing AI agents without realizing the security exposure it creates.
What tools and frameworks support AI red teaming?
The AI red teaming tool landscape has matured fast. The most established open-source option is Microsoft's PyRIT, the Python Risk Identification Toolkit, built and battle-tested by the Microsoft AI Red Team across more than a hundred generative AI products. PyRIT lets a security team pick a harm category, generate thousands of malicious prompts, and score the model's responses automatically, turning what used to take weeks into hours. It is not a replacement for a human red teamer; it automates the tedious volume so experts can focus on the novel attacks.
Alongside it sit other tools and frameworks, garak for LLM vulnerability scanning, Promptfoo for wiring adversarial checks into pull requests, and a growing set of red teaming solutions aimed at continuous testing. The pattern most mature teams land on is hybrid red teaming: automated adversarial testing for breadth and regression coverage, manual red teaming for depth and creativity. Automated red team passes catch the known attack classes on every release; humans go hunting for what the automation can't imagine yet.
On the framework side, the threat map most teams anchor to is the OWASP LLM Top 10, which catalogs the LLM security risks and red teaming targets worth prioritizing. A red teaming framework gives structure to what would otherwise be ad-hoc probing: what to test, how to score it, what evidence to keep. That last part matters more than it used to, because the evidence is increasingly a compliance artifact, not just an engineering one.
What does the EU AI Act require for adversarial testing?
Red teaming has crossed from best practice into legal obligation. Under the EU AI Act, providers of general-purpose AI models with systemic risk must conduct and document adversarial testing, with red teaming named explicitly as the example. The EU AI Act requires that this testing be retained as part of the model's technical documentation, which means red team findings are no longer internal notes; they're evidence a regulator can ask to see.
For security leaders, the practical takeaway is that red teaming efforts now need a paper trail. It is not enough to run an AI red teaming exercise and fix what you find. You have to document the methodology, the attacks attempted, the results, and the mitigations, because the AI Act treats undocumented testing as effectively untested. Budgeting for red teaming as a recurring, evidenced activity, rather than a pre-launch scramble, is the compliance-grade posture.
This is where AI red teaming connects to the broader governance picture. The same documented adversarial testing that satisfies the EU AI Act also maps onto a structured AI risk management framework, which is what turns scattered red teaming activities into a defensible program. Regulators across jurisdictions are converging on the same substance: documented testing, integrated into governance, with results retained.
How does red teaming fit into an AI risk management framework?
The clearest operating model comes from the NIST AI Risk Management Framework, which organizes AI risk work into four functions: govern, map, measure, and manage. Red teaming lives mostly in measure, you are measuring how the system behaves under adversarial conditions, but its findings feed manage, where you decide what to mitigate, and govern, where you set the policies that say what level of testing a given AI system requires before AI deployment.
Slotting red teaming into the NIST AI structure does something useful: it stops red teaming from being a one-off event and makes it part of the AI development lifecycle. You map the risks relevant to your use case, measure them through adversarial testing, manage the gaps, and govern the whole loop with clear ownership. A security assessment built this way produces both robust AI security and the documentation that responsible AI and trustworthy AI claims actually require. Saying a system is safe is not the same as showing the tests that back it.
Framed this way, AI red teaming becomes the evidence engine for your AI security posture. Every red team exercise generates findings that update your risk picture, and every mitigation gets re-tested in the next cycle. That feedback loop is the difference between a program that passes one audit and one that stays secure as the model, the prompts, and the connected tools keep changing.
What does continuous AI red teaming look like in practice?
Continuous red teaming means red teaming on every meaningful change, not once before launch. Models get updated, system prompts get rewritten, new tools get connected, and each change can reopen a vulnerability you already closed. Mature teams wire automated adversarial testing into their pipeline so a quick scan runs on relevant pull requests, while deeper manual red teaming happens on a scheduled cadence for critical AI applications. Fast feedback for breadth, periodic depth for the attacks automation misses.
The organizational version of this often pairs red and blue teams: the red team finds the holes, the blue team builds the detections and guardrails, and each round of red team testing validates whether the last round of fixes actually held. Your defenses have to work every time; an attacker only needs to get through once. Continuous AI red teaming is how you keep that asymmetry from compounding against you as your AI footprint grows.
For most enterprises, the honest constraint is capacity. Building advanced red teaming in-house, the tooling, the adversarial expertise, the documentation discipline, is a real investment, and external red teaming can fill the gap while internal capability matures. However you staff it, the goal is the same: red teaming as part of how you ship AI, not a box checked once and forgotten. If you want help standing up effective AI red teaming inside a broader security program, our AI security consulting services are built around exactly that, and our enterprise AI security best practices guide covers how the pieces fit together.
You can also get a fast read on where your own program stands with our AI Blind-Spot Assessment, which surfaces the gaps between the AI risks you know about and the testing you actually have in place. If you'd rather talk it through with someone who has done this in regulated environments, reach out to our team and we'll help you build the red teaming practice your AI systems need.
Key things to remember
- AI red teaming is structured adversarial testing of an AI system, probing the model's behavior the way a real adversary would, to find AI security vulnerabilities before they're exploited.
- It differs from traditional red teaming: the attack surface is the model itself, the payload is often natural language, and a single exercise tests both security vulnerabilities and responsible AI harms at once.
- Core red teaming techniques include direct and indirect prompt injection, multi-turn escalation, prompt converters that disguise adversarial input, and, for agentic AI, tool and workflow abuse.
- The AI red teaming tool landscape has matured: Microsoft's PyRIT and similar open-source tools automate breadth, while manual red teaming provides depth; most teams run a hybrid red teaming model.
- The EU AI Act requires documented adversarial testing for general-purpose AI models with systemic risk, so red team findings are now compliance evidence, not just internal notes.
- The NIST AI Risk Management Framework gives red teaming a home in govern, map, measure, and manage, turning one-off tests into a continuous program across the AI development lifecycle.
- Continuous red teaming, automated checks on every change plus periodic deep manual testing, is what keeps AI systems secure as models, prompts, and connected tools evolve.
