Frontier AI developers operate at the intersection of rapid technical progress, extreme risk exposure, and growing regulatory scrutiny. While a range of external evaluations and safety frameworks have emerged, comparatively little attention has been paid to how internal organizational assurance should be structured to provide sustained, evidence-based oversight of catastrophic and systemic risks. This paper examines how an internal audit function could be designed to provide meaningful assurance for frontier AI developers, and the practical trade-offs that shape its effectiveness. Drawing on professional internal auditing standards, risk-based assurance theory, and emerging frontier-AI governance literature, we analyze four core design dimensions: (i) audit scope across model-level, system-level, and governance-level controls; (ii) sourcing arrangements (in-house, co-sourced, and outsourced); (iii) audit frequency and cadence; and (iv) access to sensitive information required for credible assurance. For each dimension, we define the relevant option space, assess benefits and limitations, and identify key organizational and security trade-offs. Our findings suggest that internal audit, if deliberately designed for the frontier AI context, can play a central role in strengthening safety governance, complementing external evaluations, and providing boards and regulators with higher-confidence, system-wide assurance over catastrophic risk controls.
Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies
AVERI；GovAI Oxford Martin AI Governance Initiative（牛津马丁政府人工智能治理倡议）；Transluce；Independent Clear-Eyed AI（独立清晰之眼人工智能）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Alan Turing Institute（艾伦·图灵研究所）；University of Oxford（牛津大学）；Apollo Research（阿波罗研究）；University of Cambridge（剑桥大学）；METR；Foundation for American Innovation（美国创新基金会）；Institute for Law and AI（法律与人工智能研究所）；Touro University Law Center（图罗大学法律中心）；New Science（新科学）；Stanford University（斯坦福大学）；Fathom Mila, Université de Montréal（蒙特利尔大学米拉法姆）
Orphan risks at the frontier of artificial intelligence: What diverging safety and compliance frameworks reveal about how AI companies choose the risks they prioritize
School for the Future of Innovation in Society, Arizona State University（社会创新未来学院，亚利桑那州立大学）
Institute for Future Initiatives, The University of Tokyo（东京大学未来创成机构）；Deloitte Touche Tohmatsu LLC（德勤日本有限责任公司）；Toyo University（东洋大学）；CDLE, AI Legal (NEC)（日本电气股份有限公司AI法律中心CDLE）；IRCA (International Register of Certified Auditors)（国际注册审计师登记处）
Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits
Thomas Lord Department of Computer Science, University of Southern California（美国南加州大学计算机科学系汤姆·劳德部门）；Marshall School of Business, University of Southern California（美国南加州大学马歇尔商学院）
