A risk threshold is a predefined level of concern that tells a security team or AI governance team when a model is acceptable for release, when it should be restricted, and when it needs more testing. It turns vague safety concerns into a decision rule, often based on capability, misuse potential, reliability, and the quality of available evidence.
In cyber security, risk thresholds matter because they shape how powerful systems are deployed. A low threshold may keep a model behind stronger controls, while a higher threshold may allow broader access after review. Attackers can exploit weak thresholds by pushing systems just below the line during evaluation, then abusing them after release. Defenders use thresholds to gate launches, trigger red-team testing, require human review, or block unsafe features until the evidence improves. The key security question is whether the threshold is transparent, measurable, and backed by verifiable testing.