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OpenAI 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
This is OpenAI's official Preparedness Framework page, relevant to discussions of frontier AI governance, deployment safety standards, and how leading labs operationalize risk thresholds before releasing powerful models.
Metadata
Importance: 72/100organizational reportreference
Summary
OpenAI's Preparedness initiative outlines a framework for tracking, evaluating, and mitigating catastrophic risks from frontier AI models. It establishes risk thresholds across categories like cybersecurity, CBRN threats, and persuasion, and defines safety standards that must be met before model deployment.
Key Points
- •Defines a structured risk assessment framework categorizing frontier model risks into domains: CBRN, cybersecurity, persuasion/deception, and model autonomy.
- •Establishes 'safety levels' (low, medium, high, critical) for each risk category with deployment thresholds tied to those levels.
- •Mandates pre-deployment and ongoing evaluations ('evals') of frontier models to track risk profiles as capabilities scale.
- •Creates a Preparedness team responsible for conducting red-teaming, evaluations, and maintaining the safety case for model releases.
- •Represents OpenAI's institutional commitment to structured risk governance alongside its broader safety policies.
Cited by 6 pages
| Page | Type | Quality |
|---|---|---|
| AI Capability Threshold Model | Analysis | 72.0 |
| Instrumental Convergence Framework | Analysis | 60.0 |
| Corporate AI Safety Responses | Approach | 68.0 |
| Evals-Based Deployment Gates | Approach | 66.0 |
| Third-Party Model Auditing | Approach | 64.0 |
| Voluntary AI Safety Commitments | Policy | 91.0 |
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