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At a glance: three different kinds of rules
“AI safety rules” can mean both rules for how people may use an AI product and internal processes for evaluating powerful models. Those answer different questions. Acceptable-use policies govern user behavior; frontier-risk frameworks focus on what a model might enable and what a company says it will do before deployment.
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| Organization | Broad rules | Frontier-risk framework | Main trigger described |
|---|---|---|---|
| Google / Google DeepMind | Google AI Principles cover responsible development and deployment across the AI lifecycle. | Google DeepMind’s Frontier Safety Framework (FSF), version 3.1, dated April 17, 2026. | Critical Capability Levels (CCLs) for severe-risk capabilities, with Tracked Capability Levels (TCLs) used in certain domains to help identify less-extreme risks earlier. |
| OpenAI | Usage Policies set expectations for acceptable use of OpenAI products. | Preparedness Framework update dated April 15, 2025. | High and Critical capability levels, defined by the potential for severe harm or unprecedented new pathways to harm. |
| Meta | The Advanced AI Scaling Framework complements Meta’s broader AI governance work. | Advanced AI Scaling Framework version 2, with an update announced April 8, 2026. | Whether a model could substantially contribute to a defined threat scenario with catastrophic outcomes. |
The table summarizes what each organization says its framework is designed to do. A CCL, a High or Critical level, and a catastrophic-outcome threshold are different constructs, not comparable scores on a shared scale.
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How Google and Google DeepMind set thresholds
AI Principles apply across the lifecycle
Google’s AI Principles describe responsible development and deployment alongside innovation. The company lists human oversight, due diligence and feedback, safety and security research, testing, monitoring, safeguards against harmful outcomes and unfair bias, and attention to privacy, security, and intellectual property. Google presents this as governance spanning development through post-launch monitoring and remediation, rather than as a frontier-model threshold system alone.
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The Frontier Safety Framework focuses on severe capabilities
Google DeepMind’s FSF complements those broader practices by tracking capabilities that could create severe risks. Its overview describes identifying capability levels, detecting when models reach them during development and deployment, preparing mitigation plans, and potentially involving external parties. The current listed version is 3.1, dated April 17, 2026.
The updated framework uses CCLs for severe-risk capabilities and adds a CCL addressing harmful manipulation. It also expands protocols for risks related to loss of control and machine-learning research and development (ML R&D). Before relevant external launches, Google DeepMind describes safety-case reviews: a structured case for why a model can be deployed with its mitigations. In some domains, TCLs are intended to identify less-extreme risks earlier. The company says it applies mitigations before specific thresholds as part of standard model development, too.
That distinction matters: a threshold is not necessarily the point at which safety work begins. Google DeepMind describes early-warning assessment and mitigations before some CCLs are reached, with additional framework steps for relevant capability levels.
Older descriptions may not reflect the current framework
Google DeepMind’s 2024 introduction described the original FSF’s initial domains as autonomy, biosecurity, cybersecurity, and ML R&D. That is historical context, not a complete description of version 3.1: later updates changed the framework, including the addition of a harmful-manipulation CCL and expanded protocols in other areas.
How OpenAI separates model preparedness from product-use rules
Preparedness Framework levels
OpenAI’s April 15, 2025 update describes two capability levels. A High-level system could amplify existing pathways to severe harm; a Critical-level system could introduce unprecedented new pathways. OpenAI says a High-level system needs safeguards that sufficiently minimize the relevant severe risk before deployment. A Critical-level system also needs safeguards during development.
These descriptions set out OpenAI’s own categories; they do not make “High” or “Critical” equivalent to Google’s CCLs or Meta’s outcome thresholds. The framework focuses on severe risks associated with frontier capabilities, not every kind of product safety or every prohibited use.
Review and deployment decisions
OpenAI says its Safety Advisory Group reviews capability and safeguards reports, considers residual risk, and recommends whether more evaluation or stronger protections are needed. OpenAI Leadership makes the final deployment decision. The framework update says OpenAI plans to publish preparedness findings with each frontier-model release and describes the framework as subject to revision.
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Usage Policies address users, not capability thresholds
OpenAI’s Usage Policies establish expectations for acceptable use across its products. The policy page says violations can lead to loss of access or other penalties; its universal-policy update took effect October 29, 2025. These rules govern how people use services, whereas the Preparedness Framework describes how OpenAI assesses and responds to frontier-model capabilities.
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OpenAI also describes a broader safety approach that includes iterative evaluation, layered defenses, internal and external testing, red teaming, deployment criteria, monitoring, information security, and system cards. Those activities provide context around preparedness, but they do not turn user-facing policy rules into model capability thresholds.
How Meta evaluates catastrophic threat scenarios
Threat modeling and safeguards
Meta’s Advanced AI Scaling Framework version 2 focuses on catastrophic risks in chemical and biological safety, cybersecurity, and loss of control. Meta describes a process that begins by anticipating threat scenarios and relevant capabilities, then evaluating and mitigating risks, and finally deciding whether and how to deploy.
The framework’s trigger is framed around outcomes: when an assessment indicates a model could substantially contribute to a defined threat scenario, Meta says safeguards must be defined, implemented, and validated. It describes threat modeling and risk assessment as inputs to this judgment, rather than relying only on a named capability level. Governance includes centralized review involving senior decision-makers, and Meta says it will review the framework at least annually.
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In an April 8, 2026 announcement, Meta said the updated framework broadens risk evaluation, strengthens deployment decisions, and introduces Safety & Preparedness Reports. Meta says it tests against thousands of scenarios before deployment, monitors live traffic with automated systems, and layers safeguards from training-data filtering and safety-focused training through product-level guardrails. “Thousands” is the company’s stated scale; it did not give an exact count in that announcement.
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Meta says the reports will cover assessments, evaluation results, deployment rationale, and remaining limitations. Its description of live monitoring also recognizes that pre-deployment tests cannot cover every possible issue. These are statements about Meta’s process, not independent validation of its effectiveness.
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Evaluation evidence
All three companies describe pre-deployment assessment, but the evidence is organized differently. Google DeepMind describes early-warning evaluations and safety-case reviews for relevant CCLs. OpenAI describes capability and safeguard reports for review by its Safety Advisory Group. Meta describes threat modeling, risk assessments, safeguards, and centralized senior review. Meta also explicitly describes automated monitoring of live traffic; the reviewed descriptions do not establish that the other organizations lack post-launch monitoring, only that their cited framework accounts emphasize different details.
Who reviews the case
OpenAI names a Safety Advisory Group that reviews reports and recommends next steps, while OpenAI Leadership makes final deployment decisions. Meta describes centralized review involving senior decision-makers. Google DeepMind describes safety-case review before relevant external launches and says external parties may be involved, but the cited framework descriptions do not set out the same named decision chain as OpenAI’s.
What the public can see
The organizations describe different forms of disclosure. Google DeepMind links framework versions and model evaluation reports. OpenAI says it plans to publish Preparedness findings with frontier-model releases. Meta says its Safety & Preparedness Reports will present assessments, results, deployment rationale, and limitations. The existence of a reporting commitment does not by itself show how complete, comparable, or independently checked future reports will be.
Some company-reported figures provide context about activity, but they are not comparable measures of safety outcomes. Google’s February 2025 AI Responsibility Update reported more than 300 AI responsibility and safety research papers and $120 million in partnerships on AI responsibility with outside groups and institutions to date; the latter was not described as annual spending or independently audited impact. Google’s safety page attributes $10 million awarded to more than 600 researchers to its bug bounty program in 2023, and lists over 25,000 human reviewers on the page accessed October 7, 2026, without dating that headcount. Meta’s 2026 announcement says it tests thousands of scenarios, without specifying an exact total.
Can these rules show which company is safest?
No reliable ranking follows from the public policy language alone. The frameworks use different scopes, risk models, thresholds, and governance arrangements, and the reviewed sources do not provide a common independent test for comparing them. They also do not offer a shared denominator for incidents, false negatives, independent audit results, or realized safety outcomes.
A framework can show what a company says it will evaluate, what kinds of risks it prioritizes, and how it says deployment decisions are made. It cannot, by itself, prove that safeguards work as intended in practice or that one company’s system is safer than another’s.
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