AI Glossary
AI Red Teaming
The practice of actively testing AI systems to identify weaknesses, risks, and vulnerabilities.
AI Red Teaming
Overview
No system should be considered secure simply because nobody has found a problem yet.
Organizations often test systems proactively to discover weaknesses before attackers or failures expose them.
This practice is known as red teaming.
AI Red Teaming applies this concept to artificial intelligence systems.
It involves actively testing AI models, applications, and workflows to identify vulnerabilities, safety concerns, security risks, and unintended behaviors.
A helpful way to think about red teaming is a fire drill.
The goal is not assuming a disaster will occur tomorrow.
The goal is preparing in case something goes wrong.
AI red teams may test systems for prompt injection, jailbreaking, harmful outputs, data leakage, security weaknesses, and other risks.
As organizations deploy AI more broadly, red teaming is becoming an increasingly important part of responsible AI development.
Why It Matters
AI red teaming helps organizations discover and address weaknesses before they become larger problems.
Real-World Example
A company may assemble a team to intentionally challenge an AI system and evaluate how it responds under unusual conditions.