Miles Brundage, a former policy researcher at OpenAI, formally announced the AI Verification and Evaluation Research Institute (AVERI) on 15 January 2026, Fortune reported.[3] AVERI is a US nonprofit that advocates external audits of frontier AI developers, the companies that build the most capable AI systems. It is also working on auditing standards.[1, 3] Its launch coincided with a paper by Brundage and 47 co-authors.[2, 3] The paper defines frontier AI auditing as rigorous third-party verification of developers' safety and security claims, and evaluation of their systems and practices against relevant standards. Under this definition, auditors rely on deep, secure access to non-public information.[1]
The paper proposes four AI Assurance Levels (AALs) to communicate how much confidence an audit's conclusions provide.[1] An AAL-1 audit covers one system, typically over a few weeks, and the auditor still relies heavily on the company's own statements. An AAL-2 audit spans months and adds gray-box system access, meaning partial access to the system's internals. It also adds internal documents such as safety cases, and interviews with staff across several functions. Its methods include reproducing a sample of internal evaluations and developing statistical model signatures to confirm that audited models correspond to deployed ones.[2] The two higher levels add white-box access to internals such as model weights, and more extensive continuous monitoring. AAL-4 further aims to detect active deception. Its tools include tamper-evident logging and hardware attestation, in which a chip signs evidence about its identity, configuration and operating state.[2]
The authors recommend AAL-1 as a baseline for frontier AI and AAL-2 as a near-term goal for the most advanced developers. They say AAL-3 and AAL-4 are not yet technically and organizationally feasible.[1] The paper names three challenges to making them feasible: confirming that auditors receive complete information, monitoring continuously, and letting auditors examine sensitive information without exposing it. The research priorities it lists include proof-of-training techniques, change-detection infrastructure and model fingerprinting.[2]
Sources
- Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies
- Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies (arXiv:2601.11699v4)
- Exclusive: Former OpenAI policy chief creates nonprofit institute, calls for independent safety audits of frontier AI models
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