OpenAI published a framework titled “Towards safety cases for frontier AI training” on September 28, 2026, stating that “structured safety documentation should be required before continuing any frontier reinforcement learning training run.” The company calls a full safety case “an aspirational north star” and says the practices it describes “represent our current recommendations and are in the process of being implemented at OpenAI.”
The document is written as guidance for the field as well as for OpenAI’s own process. OpenAI frames it as “current learnings,” says it expects the practices to “evolve as we continue iterating on internal processes,” and invites feedback from the community.
Three parts of a safety case
OpenAI organizes the proposed safety case into three sections.
| Section | What OpenAI says it should cover |
|---|---|
| Technical safeguards | Alignment of the model in training, containment of the training run, and monitoring |
| Operational guidelines | Dissents from other teams, multi-level approvals, accountability, internal transparency, audits and escalations |
| Investigations of misalignment incidents | Root-cause analysis of training dynamics, operational postmortems and new detection methods |
Technical safeguards
Under alignment, OpenAI lists the design of training environments, automated and manual dataset reviews, measurement of alignment, and avoiding training on the model’s chain of thought. Containment covers infrastructure security, red-teaming, limits on communication between samples and immutable transcripts. Monitoring covers keeping the model’s reasoning monitorable, high recall on known issues and rapid-response protocols with defined service-level targets.
Who signs off
The operational section is the most concrete about decision rights. OpenAI describes “dissents” (pre-mortems written by other teams to find weaknesses in a safety case) and multi-level approvals in which the “research org lead / VP, Head of Safety, and Chief Scientist” can veto a run.
Accountability is assigned to a named person: “The senior leader responsible for a training run should be accountable for the safety case,” and OpenAI says this should apply “as part of performance reviews.” The section also calls for internal transparency, audits, escalation paths with severity levels and technical controls that enforce compliance.
When something goes wrong
For misalignment incidents, the framework calls for root-cause analysis of training dynamics, operational postmortems and the development of detection methods. On disclosure, OpenAI writes that “investigation results, postmortems, and operational changes should be shared with the public.”
What it changes
The framework places a documented, signed-off review at the training stage: before a frontier reinforcement learning run continues, named executives can stop it, and one senior leader answers for its safety case. The document does not name a specific training run, model or implementation date.
For OpenAI’s model releases by date, see the AI model release timeline; profiles of the labs are in AI companies.
