OpenAI has set out an early framework for building safety cases before training frontier AI models. A safety case is a structured argument, backed by evidence, that a risky activity can proceed within defined limits.
The guidelines divide the work into three areas: technical safeguards, operational practices and investigations of incidents that may indicate model misalignment. Training risk does not come only from a model’s measured capabilities. It also depends on access controls, monitoring, response procedures and whether warning signs are examined rather than dismissed.
OpenAI describes the document as an initial set of guidelines, not a finished standard. The practical test will be how clearly future training decisions disclose their evidence, thresholds and unresolved uncertainties. For now, the publication gives researchers and policymakers a concrete outline against which to compare safety claims around increasingly capable training runs.