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Preparedness Framework
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: OpenAI
OpenAI's official institutional framework for catastrophic risk evaluation; relevant for understanding how leading AI labs operationalize safety policies and set deployment guardrails for frontier models.
Metadata
Importance: 72/100organizational reportprimary source
Summary
OpenAI's Preparedness Framework outlines a structured approach to evaluating and managing catastrophic risks from frontier AI models, including threats related to CBRN weapons, cyberattacks, and loss of human control. It defines risk severity thresholds and ties model deployment decisions to safety evaluations. The framework represents OpenAI's operational policy for responsible frontier model development.
Key Points
- •Defines 'Preparedness' as the function responsible for tracking, evaluating, and forecasting catastrophic risks from frontier AI models.
- •Establishes risk categories including CBRN (chemical, biological, radiological, nuclear), cybersecurity, model autonomy, and societal disruption.
- •Sets deployment thresholds: models rated 'critical' risk cannot be deployed; 'high' risk models require safeguards before release.
- •Introduces a Safety Advisory Group and oversight structure to review evaluations and recommend deployment decisions to leadership.
- •Represents a living policy document subject to revision as capabilities and understanding evolve.
Cited by 7 pages
| Page | Type | Quality |
|---|---|---|
| AI Uplift Assessment Model | Analysis | 70.0 |
| AI Risk Critical Uncertainties Model | Crux | 71.0 |
| Frontier AI Labs (Overview) | -- | 85.0 |
| Alignment Evaluations | Approach | 65.0 |
| Corporate AI Safety Responses | Approach | 68.0 |
| Dangerous Capability Evaluations | Approach | 64.0 |
| Bioweapons Risk | Risk | 91.0 |
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Our updated Preparedness Framework | OpenAI
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OpenAI
April 15, 2025
PublicationSafety
Our updated Preparedness Framework
Sharing our updated framework for measuring and protecting against severe harm from frontier AI capabilities.
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We’re releasing an update to our Preparedness Framework, our process for tracking and preparing for advanced AI capabilities that could introduce new risks of severe harm. As our models continue to get more capable, safety will increasingly depend on having the right real-world safeguards in place.
This update introduces a sharper focus on the specific risks that matter most, stronger requirements for what it means to “sufficiently minimize” those risks in practice, and clearer operational guidance on how we evaluate, govern, and disclose our safeguards. Additionally, we introduce future-facing research categories that allow us to remain at the forefront of understanding emerging capabilities to keep pace with where the technology is headed. We will continue investing deeply in this process by making our preparedness work more actionable, rigorous, and transparent as the technology advances.
We’ve learned a great deal from our own testing, insights from external experts, and lessons from the field. This update reflects that progress. In line with our core safety principles, it makes targeted improvements that include:
Clear criteria for prioritizing high-risk capabilities. We use a structured risk assessment process to evaluate whether a frontier capability could lead to severe harm and we assign it to a category based on defined criteria. We track capabilities that meet five key criteria that make it a priority for us to prepare in advance: the risk should be plausible, measurable, severe, net new, and instantaneous or irremediable. We measure progress on these capabilities, and build safeguards against the risks that these capabilities create.
Sharper capability categories. We've updated our categorization of capabilities to apply these cri
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