AI Security Is Never Finished: Building the Continuous Red Teaming Loop
Security programs are built around moments of closure: the finding is closed, the control passed, and the release can move forward.
AI security refuses to cooperate with that model.
A passing test is useful evidence, but only for a specific system, configuration, and moment.
Production AI keeps moving. Models change behavior, prompts are revised, retrieval sources are added, agents gain tools, and users introduce unexpected context. Attackers adapt just as quickly. Yesterday’s clean result may not describe tomorrow’s risk.
That has been the practical case for continuous AI red teaming. Now, research from the National Institute of Standards and Technology provides a mathematical basis for it.
In Robust AI Security and Alignment: A Sisyphean Endeavor?, NIST senior scientist Apostol Vassilev applies the logic of Gödel’s incompleteness theorem to AI security. The conclusion is uncomfortable but useful: no finite set of guardrails can be universally robust against adversarial prompts.
That...
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