A safety framework is a formal process for evaluating risk and setting conditions before an AI model is trained, fine-tuned, or released. It usually defines what must be tested, which hazards are unacceptable, who approves the launch, and what monitoring is required after deployment. Common controls include red-teaming, capability and misuse evaluations, access restrictions, documentation, and rollback plans.
In cyber security, safety frameworks matter because AI systems can be used for both defense and attack. A weak framework may miss risks such as phishing generation, malicious code assistance, data leakage, prompt injection, or unsafe tool use. A strong framework helps defenders prove that a model has been measured against realistic abuse cases and that release thresholds are based on evidence, not assumptions. In practice, the framework turns model safety from a vague promise into a repeatable control process.