OpenAI detailed how independent organizations should vet its models. The document doubles as a checklist for enterprise AI buyers.

On September 22, 2026, OpenAI published “Priorities and principles for effective third party assessments,” authored by Lama Ahmad. The framework describes how the company engages private and non-profit assessors on technical safety work, and it is explicitly separate from OpenAI’s evaluation work with governments, which it says “may call for different approaches.”

The paper names priority areas for outside review, including independent assessment of safety cases across training and deployment, and assessment of critical safeguards. The full set also covers capability evaluations and misalignment incidents. OpenAI states it previously gave assessors deep access, including visible chain-of-thought and confidential internal deployment data for incident response and red teaming. It also sets an independence principle requiring assessors to disclose and manage conflicts of interest, with recusal or exclusion periods.

For federal and enterprise security leaders, this is usable procurement leverage. The named priority areas, the specific access types, and the conflict-of-interest rules translate directly into RFP and contract terms that force vendors to back safety claims with external, falsifiable evidence rather than self-attestation.

Treat this framework as a baseline, not a guarantee. This is vendor-authored guidance describing OpenAI’s own commitments, not a regulatory standard or an audited result, and no assessor firms are named. It is a practical benchmark to cite while pending federal AI safety-testing legislation debates whether such review should be mandated rather than invited.