> ## Content Index > Fetch the complete content index at: https://www.aigl.blog/llms.txt > Use this file to discover other available public pages before exploring further. # Preparedness Framework - URL: https://www.aigl.blog/preparedness-framework/ - Published: 2026-09-19T09:15:18.000Z - Updated: 2026-09-19T09:15:18.000Z - Description: OpenAI's Preparedness Framework (Version 2) sets out how the company identifies frontier AI capabilities that could cause severe harm, defines High and Critical thresholds, and describes the evaluations, safeguards, security controls and governance used to manage those risks. - Author: Jakub Szarmach - Tags: Framework, AI Governance, AI Safety, Risk Management, AI Security, Technical Controls, #aigl-library ## ⚑ Quick Summary Published by OpenAI, this framework sets out the company's approach to tracking and preparing for frontier AI capabilities that create new risks of severe harm, defined here as "the death or grave injury of thousands of people or hundreds of billions of dollars of economic damage". Version 2, last updated 15 April 2025, organises the work around three Tracked Categories β€” biological and chemical, cybersecurity, and AI self-improvement capabilities β€” each with a threat model and High and Critical capability thresholds, plus five Research Categories: long-range autonomy, sandbagging, autonomous replication and adaptation, undermining safeguards, and nuclear and radiological. The document describes how capabilities are measured through scalable evaluations and deep dives, how threshold determinations are compiled into a Capabilities Report, and how safeguards are selected, documented in a Safeguards Report and judged sufficient by the Safety Advisory Group (SAG), with OpenAI Leadership taking final deployment decisions and the Board's Safety and Security Committee providing oversight. Models that reach a Critical threshold require safeguards during development, irrespective of deployment plans. ## 🧩 What’s Covered - **Introduction and the case for updating:** four changes driving version 2 β€” safeguards for stronger models needing more planning and coordination, more frequent deployments needing scalable evaluations, a dynamic frontier development landscape, and accumulated experience; the SAG, OpenAI Leadership and the Board's Safety and Security Committee are introduced as the governance structure. - **Deciding where to focus:** a holistic risk assessment drawing on internal research, academic and domain experts, industry bodies such as the Frontier Model Forum and the U.S. government; five criteria for Tracked Categories β€” plausible, measurable, severe, net new, and instantaneous or irremediable β€” and the definition of Research Categories. - **Tracked Categories (Table 1):** for biological and chemical, cybersecurity and AI self-improvement, the High and Critical capability thresholds, the associated risks of severe harm, and risk-specific safeguard guidelines, including halting further development until Critical-standard safeguards are specified. - **Research Categories (Table 2):** long-range autonomy, sandbagging, autonomous replication and adaptation, undermining safeguards, and nuclear and radiological, each with a potential response; notes explain the split of the former Model Autonomy category and the removal of persuasion from the framework. - **Measuring capabilities:** the evaluation approach (highest-capability settings, a variant with negligible safety refusals, best available scaffolds), scalable evaluations versus deep dives, the testing scope for covered deployments, and SAG determinations based on the Capabilities Report. - **Safeguarding against severe harm:** safeguard selection, the contents of the Safeguards Report, SAG decision points on sufficiency, the marginal-risk clause covering other developers' High or Critical releases, and additional safeguards for models nearing Critical capability. - **Building trust:** internal transparency, the Raising Concerns Policy and noncompliance handling, public disclosures, third-party evaluation of capabilities and stress testing of safeguards, and independent expert opinions to SAG. - **Appendices:** a twelve-item change log, decision-making practices including SAG membership and the fast-track process, illustrative safeguards against malicious users and misaligned models with efficacy assessments, and security controls aligned with ISO 27001, SOC2, NIST SP 800-53 and FedRAMP. ## πŸ’‘ Why it matters? For organisations building or assessing frontier systems, the document offers a worked model of how capability thresholds, threat models, evaluations and safeguards can be linked: measure capabilities, determine whether a threshold is crossed, then justify safeguards through claims such as robustness, usage monitoring or trust-based access. Its marginal-risk clause and its references to peers' frameworks, including Anthropic's RSP and Meta's Frontier AI Framework, show how one developer positions its commitments relative to the wider field. Auditors and board committees can read it as an example of governance artefacts β€” Capabilities Reports, Safeguards Reports and defined decision points. ## ❓ What’s Missing The framework is a self-governance document: it does not publish the underlying threat models, evaluation results or numeric thresholds, and the safeguards in Appendix C are explicitly illustrative and "should not be construed as a definitive or comprehensive list". Many of the techniques "require further research". External verification is left to SAG discretion rather than being mandatory, and final deployment decisions rest with OpenAI Leadership, which can decide without SAG participation. Persuasion risks are excluded from the framework, and nuclear-related work is constrained by classified information handling. ## πŸ‘₯ Best For Best suited to frontier-lab safety and governance teams designing threshold-based deployment policies, to second- and third-party evaluators who need to understand how Capabilities and Safeguards Reports are used, and to board risk or audit committees reviewing how an AI developer documents oversight of severe-risk capabilities. ## πŸ“„ Source Details *Preparedness Framework*, Version 2, published by OpenAI; last updated 15 April 2025\. No individual authors are named. The document runs to 22 pages in English and carries no reference number or URL. Its change log lists twelve updates relative to the previous version. The input was a full text extraction of all 22 pages, so tables such as the Tracked Category and Research Category tables were read as extracted text rather than as formatted layouts.