As artificial intelligence (AI) capabilities accelerate rapidly, policymakers and AI leaders are calling for a new AI transparency framework explicitly aimed at developers of frontier models, the most potent and resource-intensive AI systems. The proposed framework seeks to strike a balance between safety, public accountability, and continued innovation without stifling progress in areas such as drug discovery, defense, and public services.

Why is AI Transparency Urgent?

The development of advanced AI systems poses significant societal and national security risks, especially in the absence of clearly defined safety and governance standards. As global governments, academic bodies, and industry leaders work toward long-term regulation, experts emphasize the need for interim steps to ensure these powerful systems are developed safely and transparently.

The proposed framework introduces a lightweight, flexible approach to regulation, focusing solely on large-scale AI developers whose models meet specific thresholds, such as computing power, R&D spending, or annual revenue. It deliberately avoids rigid, prescriptive mandates to keep pace with the fast-evolving nature of AI technologies.

Key Elements of the Proposed Transparency Framework

1. Focus on Largest Frontier Model Developers

The framework targets only the most advanced model creators, those with computing resources, R&D investments, or revenues exceeding significant thresholds (e.g., $100M+ in annual revenue or $1B+ in annual R&D). Smaller AI startups and low-risk models would be exempt to avoid stifling innovation.

2. Secure Development Framework (SDF)

Covered organizations would be required to create and publish a Secure Development Framework outlining how they identify and mitigate risks such as.

• Chemical, biological, radiological, and nuclear misuse
• Model autonomy misalignment
• Public safety threats

This SDF must remain public (with reasonable redactions) and updated as models evolve.

3. Public Disclosure and Self-Certification

Developers would publicly share their SDF on a registered company website and self-certify compliance. This gives governments, researchers, and the public insight into the lab’s AI safety protocols.

4. System Cards for Each Model

Each model deployment would include a “system card,” a document summarizing.

• Safety tests
• Evaluation results
• Risk mitigations

These cards would be required at launch and updated upon substantial revisions.

5. Whistleblower Protections

To ensure integrity, it would become illegal to falsify compliance with the safety framework. This enables existing whistleblower protections and focuses enforcement on purposeful misconduct.

Why Does This Approach Work?

The proposed AI transparency framework aligns with best practices already employed by leading labs, including Anthropic, OpenAI, Microsoft, and Google DeepMind. Making these voluntary disclosures a legal requirement ensures.

• Baseline accountability across the industry.
• Transparency for policymakers and the public.
• Adaptability to future breakthroughs and risks.

It avoids prematurely locking in outdated safety practices and leaves room for future consensus-building between stakeholders.

Looking Ahead: Safe AI Innovation at Scale

As AI systems become increasingly central to sectors such as healthcare, defense, and finance, the risk of a catastrophic failure, if left unchecked, could erode public trust and hinder global progress. This transparency-first policy offers a practical solution: enhancing visibility into safety practices while preserving the private sector’s ability to lead innovation.

Policymakers are encouraged to consider the framework at the federal, state, or international level, establishing a global model for the responsible deployment of AI without compromising agility or speed.