Leadership · 4 minute read
AI Red Teaming Explained for Executives
AI red teaming is adversarial testing: deliberately trying to make a system misbehave, exceed its permissions, leak data, or be manipulated. It complements evaluation, which measures normal performance, and it should be run before launch for high-risk agents, repeated on a schedule, and read as a design input rather than as a verdict.
Evaluation tells you whether a system works when used as intended. Red teaming tells you what happens when someone tries to make it fail. Both are necessary, and executives frequently fund the first and skip the second, which is how agents with broad permissions reach production untested against the attacks they will actually face. This explainer covers what red teaming tests, who should do it, and how to read the results.
What does red teaming test?
Adversarial behavior across several dimensions:
| Dimension | Example test | What a finding means |
|---|---|---|
| Prompt injection | Instructions hidden in a document the agent reads | Whether content can redirect the agent's actions |
| Permission boundaries | Attempts to make the agent act beyond its scope | Whether scope is enforced or merely instructed |
| Data leakage | Attempts to extract information the user should not see | Whether retrieval respects permissions |
| Harmful output | Attempts to elicit unsafe, offensive, or non-compliant content | Whether output controls hold under pressure |
| Tool misuse | Attempts to have tools used in unintended combinations | Whether tool design bounds consequences |
| Denial and cost | Attempts to trigger expensive loops | Whether limits and stop conditions work |
| Social engineering | Impersonation and pretexting through the agent | Whether verification requirements hold |
The prompt injection explained for executives piece covers the first and most important category.
Why is it different from evaluation?
Evaluation samples representative inputs and measures correctness; red teaming searches for the inputs that break things. A system can pass evaluation at a high rate and still be trivially manipulable, because the evaluation set contains no adversary. Conversely, a system with a modest pass rate but tightly bounded permissions may be perfectly safe to deploy under review.
The two answer different executive questions: evaluation answers "does it work well enough to be useful?" and red teaming answers "what is the worst an adversary can cause?" The AI evaluation explained for executives piece covers the first.
Who should do it?
People independent of the build team. Builders know the intended paths too well, test the cases they designed for, and have an incentive not to find problems before launch. Options: an internal security function with AI expertise, a specialist external provider, or both in combination for high-risk systems. Whoever does it needs access to the real system with real tools, not a sanitized version, or the test proves little. The AI penetration testing scope guide covers scoping an engagement.
How often?
Before launch for high-risk agents, particularly anything customer-facing or acting with consequence; after significant changes to permissions, tools, or models, including provider model updates; and on a schedule thereafter, at least annually and more often for externally exposed systems. Every finding should also become a permanent case in the evaluation set, so the same weakness is caught automatically in future.
How should findings be read?
Calmly, and as design input. A red team that finds nothing usually tested badly; findings are the expected output. The question executives should ask about each finding is not "can the model be tricked?" but "what is the consequence when it is?"
- A finding that the agent can be persuaded to produce an odd summary: low consequence, fix by refining controls.
- A finding that the agent can be persuaded to send an external email: high consequence, fix by removing or gating the capability.
- A finding that retrieval returns documents the user should not see: critical, fix immediately.
The pattern is that serious findings are usually fixed by changing permissions and controls, not by adjusting prompts. A prompt-level fix to a permission-level problem is a patch on the symptom. The AI guardrails explained for executives piece covers the control layer.
What should the report contain?
Findings ranked by consequence, with the exact reproduction steps, the control that failed, the recommended fix, and whether the fix is a permission change, a control addition, or a prompt adjustment. Plus a statement of coverage: what was tested and what was not, so executives know the limits of the assurance.
What should executives ask?
- Which of our agents have been red teamed, and when?
- Who did it, and were they independent of the build team?
- What was the highest-consequence finding, and how was it fixed?
- Did the fixes change permissions, or only prompts?
- Are the findings now permanent cases in the evaluation set?
How can FISTA Solutions help?
FISTA Solutions builds AI agents with adversarial cases in the evaluation set from the start and bounded permissions that limit what any successful attack can achieve, and its AI enablement practice runs adversarial reviews of agents built elsewhere, reporting findings by consequence with the control-level fix. Since 2017, FISTA has delivered 150+ projects for 50+ companies across 12+ countries.
To have an independent adversarial review of an agent before it launches, talk to FISTA on WhatsApp, or read the CISO's guide to AI and agentic AI.
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01What is AI red teaming?
Deliberate adversarial testing of an AI system: attempting to make it produce harmful output, exceed its permissions, leak information, follow injected instructions, or behave in ways its designers did not intend. It is done by people whose objective is to break the system rather than to confirm it works.
02How is red teaming different from evaluation?
Evaluation measures how often the system produces correct outcomes on representative cases, which is a quality question. Red teaming asks what an adversary or an unusual situation can cause, which is a security and safety question. A system can pass evaluation comfortably and fail red teaming badly.
03Who should run AI red teaming?
People independent of the build team: an internal security function with AI expertise, a specialist external provider, or both. The team that built the system knows its intended paths too well to attack it effectively, and has an incentive not to find problems.
04How often should AI systems be red teamed?
Before launch for high-risk agents, after significant changes to permissions, tools, or models, and on a schedule thereafter, typically at least annually and more often for externally-exposed, high-consequence systems. Findings should also become permanent cases in the evaluation set.
05How should executives read red team findings?
As design input rather than as a verdict. Findings are expected; the question is whether the consequence of a successful attack is bounded by permissions and gates. A finding that the model can be persuaded to say something odd matters less than a finding that it can be persuaded to move money.
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