Establishes AI Safety Level Standards (ASLs) for AI model testing, deployment and security. Requires ASL-2 standards for current models, with ASL-3 for higher-risk capabilities like Chemical, Biological, Radiological, and Nuclear (CBRN) weapon development or autonomous R&D. Implements Capability Thresholds to trigger safeguard upgrades. Outlines regular assessments, reviews, and transparency requirements.
Analysis summaries, actor details, and coverage mappings were LLM-classified and may contain errors.
This is an internal corporate policy document establishing Anthropic's own responsible scaling framework. It uses mandatory language ('will', 'must', 'shall') but these obligations are self-imposed by the company rather than legally binding external requirements. The document establishes internal governance structures, reporting mechanisms, and decision-making processes for the company's AI development activities.
The document has good coverage of approximately 8-10 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.
This is an internal corporate policy governing Anthropic's own operations as an AI development company. The primary sectors governed are Information (AI/technology development) and Scientific Research and Development Services (AI research). The policy does not regulate external sectors but rather establishes governance for the company's AI model development, deployment, and security practices.
The document comprehensively covers Build and Use Model, Verify and Validate, Deploy, and Operate and Monitor stages. It focuses extensively on testing models for dangerous capabilities, implementing safeguards before deployment, and ongoing monitoring post-deployment. Plan and Design is implicitly covered through capability threshold planning, while data collection is not substantially addressed.
The document explicitly covers AI models and AI systems, with detailed focus on frontier AI models. It does not explicitly mention general purpose AI, task-specific AI, foundation models, generative AI, or predictive AI by those specific terms. It extensively discusses compute thresholds using FLOP metrics and Effective Compute. Open-weight models are mentioned in the context of release decisions.
Anthropic
Anthropic is the author and proposer of this Responsible Scaling Policy. The document is their internal governance framework for AI development and deployment, as evidenced by references to 'our models', 'our risk mitigation strategy', and internal organizational structures.
Responsible Scaling Officer; CEO; Board of Directors; Long-Term Benefit Trust
Internal enforcement is conducted by designated roles within Anthropic's governance structure. The Responsible Scaling Officer has primary responsibility for ensuring policy compliance, with oversight from the CEO, Board of Directors, and Long-Term Benefit Trust.
Responsible Scaling Officer; Board of Directors; Long-Term Benefit Trust; third-party reviewers; U.S. Government entity
Monitoring involves both internal oversight (RSO, Board, LTBT) and external mechanisms (third-party reviews, government notification). The policy establishes regular assessment cycles, reporting requirements, and independent audits.
Anthropic
The policy applies to Anthropic itself - governing its own AI model development, deployment, and security practices. It regulates internal activities including model training, deployment decisions, security measures, and employee conduct.
10 subdomains (5 Good, 5 Minimal)