The Need for a Shared Technical Language

As AI models become more capable, the primary challenge shifts from theoretical safety to the practical, verifiable implementation of safeguards. Current safety efforts often suffer from fragmentation, where different organizations use disparate methods for evaluation. To address this, OpenAI has helped launch the Appia Foundation, hosted by the Linux Foundation. The core objective of Appia is to create a 'trust layer' by developing open, modular specifications. These specifications aim to translate abstract international standards into concrete, reusable assessment criteria that can be applied across the entire AI value chain—from infrastructure providers to application developers.

Operationalizing Safety Through Standardized Evaluation

Effective governance requires more than just internal policy; it demands rigorous, third-party validation. OpenAI advocates for a model where national institutions, such as the U.S. Center for AI Standards and Innovation (CAISI), act as hubs for technical expertise and independent assessment.

To make these assessments reliable, the industry must adopt a standardized approach to disclosure. OpenAI’s 'shared playbook' for third-party evaluations mandates that assessments must explicitly disclose:

• The specific system being tested.
• The tool access and evaluation harness used.
• The methods employed to elicit capabilities.
• The resources available to the model.
• The validation checks performed to verify results.

By standardizing these inputs, the industry can move toward a common technical understanding, allowing governments and independent bodies to compare performance metrics across different models and jurisdictions with higher confidence.

Building an Interoperable Ecosystem

Beyond the Appia Foundation, OpenAI is contributing to a broader ecosystem of pre-standardization efforts, including the ISO/IEC Joint Technical Committee, the NIST-led AI Safety Institute Consortium, and the Frontier Model Forum. The ultimate goal is to bridge the gap between internal safety frameworks—such as OpenAI’s 'Preparedness Framework' and 'Frontier Governance Framework'—and external regulatory requirements. By fostering interoperability, these efforts ensure that safety practices are not siloed within individual companies but become a baseline expectation for the entire AI industry.