Summary: SaferAI — Frontier AI Risk Management Ratings
Source metadata
- URL: ratings.safer-ai.org
- Organization: SaferAI (safer-ai) — a France-based nonprofit focused on AI risk-management governance and research
- Assessment team: Lily Stelling and Malcolm Murray (assessors), reviewed by Siméon Campos and Henry Papadatos; project led by Henry Papadatos
- Edition cited: October 2025 ratings
TL;DR
SaferAI publishes a standing comparative rating of how well frontier AI companies implement risk management, scoring them against a four-dimension framework drawn from established safety-critical-industry practice (ratings.safer-ai.org). In the October 2025 edition, twelve companies were rated, with Anthropic highest at 35% and Cohere lowest at 8% (ratings.safer-ai.org). SaferAI’s headline finding is that all rated companies have weak to very weak risk-management practices, with even the best-practice ceiling (combining the strongest measures across all firms) estimated at only ~54% (ratings.safer-ai.org). The ratings give the ai-risk-management and frontier-safety-frameworks concepts a concrete, third-party, quantified evidence base.
Key claims
- Companies are rated on four dimensions (ratings.safer-ai.org):
- Risk Identification — comprehensiveness of identified risks
- Risk Analysis & Evaluation — clarity on acceptable risk levels and measurable thresholds
- Risk Treatment — adequacy of mitigations and continuous monitoring
- Risk Governance — organizational structures, accountability, and transparency
- October 2025 scores (out of 100%): Anthropic 35, OpenAI 34, G42 24, Meta 21, Google DeepMind 20, Microsoft 18, Amazon 18, xAI 16, NVIDIA 16, Magic 11, Naver 10, Cohere 8 (ratings.safer-ai.org).
- All companies score weak to very weak — no firm approaches the practical best-practice ceiling of ~54% that would result from adopting every mitigation currently dispersed across the industry (ratings.safer-ai.org).
- The cohort is anchored to a concrete commitment: the rated companies are largely those that committed at the 2024 AI Seoul Summit to publish safety frameworks by February 2025 (ratings.safer-ai.org).
Methods
SaferAI scores each company by mapping public artifacts — primarily published frontier safety frameworks and related disclosures — against the four-dimension rubric, then aggregating to a percentage (ratings.safer-ai.org). The rubric mirrors the risk-management cycle (identification → analysis → treatment → governance) standard in nuclear, aviation, and pharmaceutical safety, adapted for frontier AI (ratings.safer-ai.org).
Limitations
- Public-information-bound. The ratings assess what companies disclose, not internal practice that is unpublished. A low score can reflect weak disclosure rather than (or in addition to) weak practice.
- Rubric-dependent. Scores depend on SaferAI’s weighting of the four dimensions; a different rubric would reorder firms.
- Snapshot. Frameworks change; the October 2025 numbers are a point-in-time reading.
How this updates our concepts/agendas
This is the canonical third-party quantification of the gap that the ai-risk-management page already gestures at. That concept page asserts “SaferAI provides comparative analysis of frontier AI labs against these dimensions” and “SaferAI’s comparative analysis quantifies the gap” — this summary supplies the primary-source citation and the actual numbers behind those statements. It directly informs frontier-safety-frameworks (the published FSFs are the main artifact being graded) and responsible-scaling-policy (Anthropic’s RSP is the highest-scoring instance, yet still only 35%). It is also evidence for the ai-governance open question of whether voluntary commitments survive competitive pressure: uniformly weak scores suggest voluntary frameworks are, as implemented, far from adequate.