Strengthening our Frontier Safety Framework

Our updated Preparedness Framework

Sharing our updated framework for measuring and protecting against severe harm from frontier AI capabilities.

Frontier AI regulation: Managing emerging risks to public safety

Frontier Model Forum

We’re forming a new industry body to promote the safe and responsible development of frontier AI systems: advancing AI safety research, identifying best practices and standards, and facilitating information sharing among policymakers and industry.

Frontier risk and preparedness

To support the safety of highly-capable AI systems, we are developing our approach to catastrophic risk preparedness, including building a Preparedness team and launching a challenge.

Our approach to AI safety

Ensuring that AI systems are built, deployed, and used safely is critical to our mission.

A blueprint for democratic governance of frontier AI

OpenAI outlines a blueprint for U. S.

OpenAI’s Frontier Governance Framework

Explore OpenAI’s Frontier Governance Framework and how our AI safety, security, and risk practices align with emerging EU and California regulations.

Harmonizing AI Safety Thresholds

arXiv:2607. 16112v1 Announce Type: new Abstract: Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies.

Safety and alignment in an era of long-horizon models

OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.

Why responsible AI development needs cooperation on safety

We’ve written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. Our analysis shows that industry cooperation on safety will be instrumental in ensuring that AI systems are safe and beneficial, but competitive pressures could lead to a collective action problem, potentially causing AI companies to under-invest in safety.

Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies

arXiv:2505. 22829v2 Announce Type: replace-cross Abstract: This paper bridges distribution shift and AI safety through a comprehensive analysis of their conceptual and methodological synergies.

Strengthening our safety ecosystem with external testing

OpenAI works with independent experts to evaluate frontier AI systems. Third-party testing strengthens safety, validates safeguards, and increases transparency in how we assess model capabilities and risks.