An evidence threshold is the minimum level of testing, validation, or confidence required before an AI system is approved for use. It helps decision-makers answer a simple question: is this model reliable enough for this task, with this data, under these risk conditions?
In cyber security, evidence thresholds matter because AI tools can affect detection, access control, incident triage, and code or content workflows. A low threshold may let an untested model into production, increasing the chance of errors, data exposure, or unsafe decisions. A higher threshold usually means stronger evaluation against real inputs, edge cases, and abuse scenarios such as prompt injection or model poisoning. Defenders use evidence thresholds to make approvals repeatable and auditable, while attackers may try to exploit weak review processes. The goal is not perfection; it is enough proof to justify deployment for the intended risk level.