Red Team Methodology

How to systematically test your AI system: threat modeling, attack trees, and severity scoring

What Is Red Teaming?

Red teaming is the practice of deliberately attacking your own system to find vulnerabilities before real attackers do. In AI security, this means systematically testing your AI application with adversarial prompts, injection techniques, and abuse scenarios.

The difference between ad-hoc testing and red teaming is methodology. Ad-hoc testing is trying random attacks and seeing what sticks. Red teaming follows a structured process: identify threats, build attack plans, execute systematically, score results, and document findings.

Real-world analogy: A fire drill is not someone randomly pulling the alarm. It is a planned exercise with scenarios, objectives, and evaluation criteria. AI red teaming is a fire drill for your AI system — structured, repeatable, and focused on finding real weaknesses.

Step 1: Threat Modeling

Before you attack, understand what you are defending. Threat modeling maps your system's assets, entry points, and potential attackers:

What assets are at risk?

Customer data, system prompts, API keys, business logic, tool access, reputation. List everything the AI can access or affect.

Who are the attackers?

Curious users, malicious customers, competitors, automated bots, insider threats. Each has different skills, motivation, and access.

What are the entry points?

Chat input, uploaded files, API parameters, external data sources (RAG), webhook payloads. Everywhere untrusted data enters the system.

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