OpenAI Priorities and Principles for Effective Third-Party Assessments

TL;DR

OpenAI released a framework that defines four priority assessment areas—safety case review, safeguard evaluation, capability and alignment testing, and misalignment incident investigation—and eight guiding principles to ensure third‑party assessments are independent, rigorous, and actionable.

Priority Area 1 – Independent Assessment of Safety Cases

Conclusion: Independent reviewers must evaluate the evidence supporting OpenAI’s safety claims across training, evaluation, internal deployment, and external deployment.

Assessors need expertise in alignment, control methods, cybersecurity, and misuse domains (biological, chemical, etc.).
Reviews should verify that safety‑case conditions were followed during model development and deployment.
Assessors must check whether safety cases address the most urgent identified risks and identify any gaps.
Evaluation includes whether incentives in training that could reward deception, hacking, or restriction circumvention are effectively mitigated.

Priority Area 2 – Assessment of Critical Safeguards

Conclusion: Third parties should test the robustness of OpenAI’s safeguard stack—including model‑level, enforcement, and security safeguards—under realistic conditions.

Using “grey‑box” access, assessors must probe safeguards against adversarial attacks (e.g., jailbreaks) and capability uplift in high‑risk domains such as cyber and bio.
Authorized testing should examine interactions with cyber defenses (access controls, sandboxing, detection, and response systems) to identify failures.
Reviews must identify gaps in misalignment monitors and evaluate the reliability of chain‑of‑thought monitoring as models improve.
Safeguards must be consistently applied across training, evaluation, and deployment and be proportionate to model capabilities.

Priority Area 3 – Assessment of Capability and Alignment Evaluations

Conclusion: Evaluations must continuously cover Preparedness risk categories (chemical, biological, cybersecurity, AI self‑improvement) and severe misalignment risks.

Assessors should verify that evaluation thresholds match the defined risk thresholds and are updated when models surpass existing benchmarks.
New tests must meaningfully measure advanced capabilities rather than repeating saturated evaluations.
Alignment evaluations need to capture severe misalignment behaviors and highlight any missing risk dimensions.

Priority Area 4 – Independent Investigation of Critical Misalignment Incidents

Conclusion: Independent forensic investigations of misalignment incidents provide evidence on safeguard failures and inform future safety cases.

Investigations require expertise in cyber forensics, alignment analysis, large‑scale chain‑of‑thought examination, and rapid response capabilities.
Access to sensitive internal and third‑party data may be necessary, with appropriate confidentiality safeguards.
Findings should clarify the root causes of the incident and assess whether existing safeguards could mitigate similar future events.

Principles for Effective Assessments

Conclusion: Assessments must be scoped, transparent, secure, and produce actionable recommendations.

Clear Scope and Mutually Agreed Claims – Both lab and assessor pre‑register safety claims and define what is in‑scope; out‑of‑scope items are documented with justification.
Proportionate Access – Assessors receive the minimum necessary data and system access, respecting legal, security, and IP constraints; alternative privacy‑preserving mechanisms are acceptable.
Transparent Methodology and Standards – Methods, criteria, and uncertainties are fully disclosed; where standards are absent, assessors justify their chosen metrics.
Expertise and Independence – Assessors demonstrate relevant technical expertise and disclose conflicts of interest; compensation structures must not bias outcomes.
Security and Confidentiality – Robust information‑security practices protect IP and sensitive data; when needed, assessments can be conducted on company‑controlled devices.
Actionable Findings and Remediation Time – Reports identify specific gaps, provide detailed remediation guidance, and allow labs a reasonable period to address issues before public release.
Responsible Publication – Reports are shared as openly as possible while redacting genuinely sensitive information; redaction policies are transparent and note any substantive omissions.

The Road Ahead

Conclusion: OpenAI will continue to nurture a diverse ecosystem of independent assessors and collaborate on international safety standards.

OpenAI commits to supporting deeper assessments across the four priority areas and to refining shared standards through both private governance and future legislation.
No single assessor can cover all frontier safety questions; a collaborative, multi‑assessor approach is essential.
Ongoing conversations with multiple third‑party organizations aim to align proposals with the outlined priorities and principles.

This summary reflects OpenAI’s publicly released priorities and principles for third‑party assessments as of September 22 2026.