AI Red Teaming
Systematically testing AI systems for vulnerabilities, biases, and harmful outputs.
Definition
AI red teaming involves deliberately attempting to elicit problematic behaviors from AI systems to identify vulnerabilities before deployment. Red teams probe for security weaknesses, bias patterns, jailbreaks, and edge cases that could cause harm or embarrassment.
This practice has become essential for responsible AI deployment, with major companies maintaining dedicated red teams and engaging external security researchers. The goal is to discover and fix issues before malicious actors can exploit them.
Why It Matters
Every AI system has potential failure modes that aren't obvious during development. Red teaming provides a structured approach to finding these issues before they impact users or damage your brand.
For businesses deploying AI, red teaming is increasingly expected by customers, regulators, and partners as a standard safety practice.
Examples in Practice
Before launching a public chatbot, a company engages external researchers to attempt prompt injections and data extraction attacks.
An HR team red teams their AI screening tool specifically for demographic bias across protected categories.
A financial institution stress-tests their AI advisory system with adversarial market scenarios and manipulation attempts.