OpenAI has published priorities and principles for independent technical safety assessments in the private and non-profit sector. The post is a control plane for who may challenge safety claims with standing access across training, evaluation, and deployment — not a scorecard, and not a named-partner list.
What OpenAI Published
On September 22, 2026, Lama Ahmad described OpenAI's priorities and principles for third-party assessments. The company frames the work as part of pacing the frontier, with deep access across training, evaluation, and deployment.
The post defines a safety claim against a safety case: a structured argument that connects claims to evidence across train, eval, and deploy. It complements government testing work. OpenAI says it is in conversation with multiple third parties. It does not publish assessment results or a named partner list.
Four Priority Areas
- Independent assessment of safety cases spanning training, evaluation, and internal and external deployment.
- Assessment of critical safeguards — model, enforcement, security, and misalignment monitors — under grey-box and realistic agent conditions.
- Assessment of Preparedness capability evals (chem/bio, cyber, AI self-improvement) and alignment evals for severe misalignment.
- Independent investigation of critical misalignment incidents, with expertise, access, and a remediate-before-publish posture.
The Principles
OpenAI's stated principles include:
- Pre-registered / mutually agreed claims.
- Proportionate access.
- Transparent methodology.
- Expertise and independence, including conflicts of interest.
- Security and confidentiality.
- Actionable findings, with time to remediate before publish.
- Responsible publication and redaction.
What The Post Does Not Prove
- Priorities and principles are not published assessment results, and this post does not name partners.
- This is OpenAI's public control plane for who may challenge safety claims with standing access. It is not Anthropic's embedded-evaluator capacity arrangement with Accenture.
- It is complementary to OpenAI's model-misalignment reporting process.
- A lab assessment-principles post is not a substitute for a local verify loop on agent code.
Related: See our notes on Anthropic embedding Accenture as an independent evaluator, OpenAI's model-misalignment reporting framework, and the verify-loop playbook for CI on agent-written code.