Meta Frontier AI Framework (Version 1.1)

• Authority: Private-sector companies
• Collections: Corporate policies and commitments;Editors' Picks
• Most recent activity: Enacted
• Most recent activity date: 2025-02-03

Risk subdomain coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the risk subdomain are explicitly described.

• AI system security vulnerabilities
• Disinformation, surveillance, and influence at scale
• Cyberattacks, weapon development or use
• Fraud, scams, and targeted manipulation
• Governance failure
• AI possessing dangerous capabilities
• Lack of capability or robustness

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.

• Compromise of privacy
• Competitive dynamics
• AI pursuing its own goals
• Lack of transparency or interpretability

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has good coverage of approximately 10-12 subdomains, with strong focus on malicious actors (4.1, 4.2, 4.3), AI system security (2.2), competitive dynamics (6.4), governance failure (6.5), and AI safety failures (7.1, 7.2, 7.3). Coverage is concentrated in security, misuse prevention, and AI safety domains, with particular emphasis on catastrophic risks from frontier AI models.

Sector coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.

• Information
• Scientific Research & Development Services

All other sectors had no coverage in this document.

Sectors Summary

This is an internal corporate policy document that governs Meta's own operations as an AI developer and information services company. The primary sectors governed are Information (where Meta operates) and Scientific Research and Development Services (Meta's FAIR Lab). The document does not regulate external sectors but rather establishes internal governance for Meta's frontier AI development activities.

Legislative Status

Other

Legislative Status Summary

This is an internal corporate policy document establishing Meta's own framework for managing frontier AI risks. It contains voluntary commitments and internal governance processes rather than legally binding obligations with external enforcement.

Actors

Proposer

• Entity Types: AI_Developer
• Entities: Meta
• Summary: Meta is the author and proposer of this framework, as evidenced by references to 'our Frontier AI Framework' and 'our wider AI governance program' throughout the document. Meta is developing frontier AI models and establishing internal governance processes.

Target

• Entity Types: AI_Developer
• Entities: Meta's internal research teams; Meta's product teams; FAIR Lab
• Summary: The framework applies to Meta's own frontier AI development activities, including both research and product teams. It governs internal processes for developing, evaluating, and releasing frontier AI models.

Enforcer

• Entity Types: AI_Developer
• Entities: Meta leadership teams; Multi-disciplinary review teams
• Summary: Enforcement is conducted internally by Meta's leadership and multi-disciplinary teams who review risk assessments and make decisions about model development and release. There is no external enforcement body.

Monitor

• Entity Types: AI_Developer, AI_Governance_Actor
• Entities: Meta internal teams; External subject matter experts; External experts
• Summary: Monitoring is primarily conducted by Meta's internal teams throughout the development lifecycle, with involvement of external experts for specific evaluations and threat modeling exercises. The framework also references engagement with governments and the wider AI community.

AI Lifecycle Stages

Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems

• Plan and Design
• Build and Use Model
• Verify and Validate
• Deploy
• Operate and Monitor

AI Lifecycle Summary

The document comprehensively covers all stages of the AI lifecycle with particular emphasis on evaluation, deployment, and monitoring. It describes detailed processes for planning (threat modeling, reference class identification), evaluation and mitigation throughout development, deployment decisions based on risk thresholds, and post-deployment monitoring.

Technical Scope

Terms relating to technical scope that are mentioned or implicitly referenced in the document

• AI Models
• Frontier AI
• AI Systems
• General Purpose AI
• Generative AI
• Open Weight / Open Source

Technical Scope Summary

The document explicitly focuses on frontier AI models and systems, which are defined as highly capable general-purpose generative AI models. It does not use the terms 'foundation models' or 'GPAI' but the description aligns with these concepts. The framework addresses both open-weight releases and includes implicit compute considerations through capability-based thresholds rather than explicit FLOP thresholds.