Predeployment evaluation is testing done before a model or system is released to users. The goal is to decide whether it meets required safety and risk thresholds, not just whether it performs well on benchmarks. Evaluators may probe for harmful instruction following, jailbreak resistance, data leakage, autonomy, or other behaviors that could create security risk.
In cyber security, this matters because models can assist both defenders and attackers. A model that passes capability tests but fails safety review may be blocked from deployment, kept in a restricted environment, or require additional safeguards. Real-world predeployment evaluation often includes red-teaming, abuse-case testing, and policy checks so dangerous capabilities are caught before release. Done well, it reduces the chance that a flawed AI system is exposed in sensitive workflows, where even small weaknesses can be exploited.