AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Red teaming is deliberately attacking your own AI system to find ways it can be tricked, misused, or made to fail before real users or attackers do. It is standard practice for any AI that handles sensitive data or decisions. Finding the failure yourself is far cheaper than finding it in the press.
Red teaming is hiring someone to try to break into your building before an actual burglar does. You control the conditions, learn the vulnerabilities, and fix them before it matters.
Before launching a customer-facing AI assistant, a team runs three days of structured adversarial testing. They find the model will output a competitor's pricing if prompted a specific way. Fixed before launch.
Before any public or customer-facing AI launch, and after significant capability additions. Treat it as a required phase of any AI product build, not an optional extra.
Red teaming finds the vulnerabilities you know to look for. It does not find every possible attack vector. Ongoing monitoring and incident response are the necessary complement.
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