Key insight

The red team plays the attacker to find weaknesses safely; the blue team defends and detects. The purple team is not really a third team but a way of working — red and blue sharing what they learn, in the open, so every attack that lands becomes a detection. Red-teaming and this sharing mindset are exactly how an AI agent's defenses improve, rather than being guessed once and hoped to hold.

Security teams borrow their colours from military training exercises, where one side plays the attacker and the other plays the defender, so that everyone can practise the fight in safety, long before a real one happens. Those two roles — and a third that exists to connect them — are the red, blue, and purple teams. This article explains each, and how the same idea is used to harden AI agents.

1 · The red team: the attacker on purpose

The red team plays the attacker on purpose. Their job is to try to break in, exactly as a real adversary would, using the same tricks and the same creativity — but under agreement, on a defined scope, and without causing real harm. Their goal is not to “win,” but to find the weaknesses while it is still safe to find them. A red team that fails to get in has not necessarily done a good job; a red team that finds three unexpected ways in has done a great one.

2 · The blue team: the defender

The blue team plays the defender. Their job is to detect the red team’s attacks, respond to them, and keep the systems standing — which is also their day job against real attackers, every day of the year. The exercise is, for the blue team, a rehearsal of the real thing under conditions where a mistake teaches a lesson instead of causing a breach.

The red team attacking and the blue team defending, with purple as the shared learning between them A red box labelled red team attacks on the left and a blue box labelled blue team defends on the right, with a violet band between them labelled purple: shared learning, illustrating that purple is the mixing of the two. Red teamattacks on purpose Purpleshared learning Blue teamdefends & detects
Figure 1. Red plus blue makes purple — the name itself is the lesson: the value is in the mixing, not either colour alone.

3 · The purple team: the mixing is the point

Here is the crucial insight. If the red and blue teams only ever work in secret, apart from each other, the exercise wastes half its value: the red team learns what got through, the blue team learns they were breached, but neither side automatically learns what the other now knows. The purple team exists to fix exactly that. Purple is usually not a separate standing team so much as a way of working, where red and blue deliberately share what they learned, in the open, so that every attack the red team lands immediately teaches the blue team how to detect it next time. The colour purple is simply what you get by mixing red and blue — and the name captures the whole point.

4 · A worked example: one landed attack becomes a detection

The red team discovers that a particular crafted request slips past a system unnoticed and reaches sensitive data. In a red-and-blue-only world, that finding goes into a report the blue team reads weeks later, if at all. In a purple way of working, the two teams sit together the same day: the red team shows exactly how the request was shaped, and the blue team writes a detection for that shape while the details are fresh. The single attack that landed is converted, immediately, into an attack that will be caught next time. Repeat that loop enough and the system’s defenses improve from real evidence rather than guesswork.

5 · Red, blue, and purple for AI agents

This idea extends naturally to AI agents, software that decides on its own which tools to call based on instructions written in ordinary language. Red-teaming an agent means deliberately trying to trick it with cleverly worded inputs, hidden instructions, and edge cases, to discover how it can be manipulated while it is still safe to discover. Blue-teaming an agent means building the detections and guardrails that catch those manipulations in production. And the purple mindset — sharing every successful trick so it becomes a caught case — is exactly how an agent’s defenses actually improve over time, rather than being guessed at once and hoped to hold forever.

6 · A simple test you can run this week

Try this before an incident forces the question

1. Pick one system, or one AI agent, you are responsible for.
2. Spend thirty minutes as the red team: honestly try to trick or break it.
3. For anything that works, immediately ask, as the blue team, “how would we detect this?”
4. Write down one new detection for the best trick you found.

7 · Glossary — every short-form term, spelled out

Red team
People who play the attacker on purpose, under agreement, to find weaknesses while it is safe to find them.
Blue team
People who defend, detect attacks, and keep systems standing — against the red team in exercises and real attackers year-round.
Purple team
A way of working where red and blue share what they learn openly, so every landed attack becomes a detection.
Red-teaming an agent
Deliberately trying to trick or manipulate an AI agent to discover its weaknesses safely.
AI agent
Software that decides, on its own, which tools to call and which actions to take, based on instructions written in ordinary language.
Key takeaways

Red plays the attacker to find weaknesses safely; blue defends and detects.
Purple is not a third team but a way of working — red and blue sharing openly so a landed attack becomes a detection.
The name says it: the value is in the mixing, not either colour alone.
Red-teaming an agent and sharing every successful trick is how its defenses improve from evidence, not guesswork.

References

  1. NIST Special Publication 800-115, Technical Guide to Information Security Testing and Assessment, National Institute of Standards and Technology. csrc.nist.gov
  2. This guide’s Self-Test Playbook — a paste-ready prompt pack for red-teaming your own agent.