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AI Glossary · Governance & compliance

What is Frontier Safety Framework?

Frontier Safety Framework

A frontier safety framework is a policy an AI developer publishes that sets dangerous-capability thresholds for its most advanced models and commits to specific safeguards, or to holding back a model, when those thresholds are reached.

Also known as: responsible scaling policy, RSP, frontier AI safety policy, frontier AI safety policies

· Updated · Chain of Thought

AI SecurityAI Evaluation & Reliability

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The core idea is an if-then commitment. A lab decides in advance which capabilities would be dangerous, for example meaningfully helping someone build a biological weapon or carry out autonomous cyberattacks, then runs evaluations on each new model. If a model crosses a threshold, stronger security and deployment safeguards must be in place before it ships, or the model is not deployed. Anthropic calls its version a Responsible Scaling Policy, first released in September 2023; Google DeepMind’s Frontier Safety Framework uses “Critical Capability Levels” across autonomy, biosecurity, cybersecurity and machine learning R&D.

These policies spread quickly. At the AI Seoul Summit in May 2024, sixteen companies signed the Frontier AI Safety Commitments, which include publishing such a framework; METR, which tracks the policies, reports that four more companies joined later. The policies differ in thresholds and enforcement, but share the pattern of capability evaluations, thresholds, safeguards and governance.

For buyers and builders, these documents are the best public signal of how a lab will handle a dangerous frontier model. Editorially, they are self-imposed: the company writes, interprets and can revise its own policy, which is why outside testing by AI safety institutes and red teaming matter. They are a specific instrument within broader AI governance.

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