AI systems are increasingly autonomous — executing transactions, managing infrastructure, interacting with the public. Yet no independent credentialing regime verifies whether they are safe, competent, and behaving within defined parameters. The Box Commons is building that infrastructure: technology-agnostic standards, third-party certification, and governance no single company controls.
Three forces are converging to make AI credentialing urgent:
Credentialing fills the gap between what AI systems can do and what the market — regulators, insurers, enterprises, and the public — needs to verify they do safely.
The Box Commons engages with federal agencies, standards bodies, and the public to advance credentialing infrastructure for AI systems. Our work spans three domains: AI evaluation standards, AI agent identity and authorization, and AI agent security.
The structural blueprint for an independent AI credentialing standards body — built on the Forest Stewardship Council's three-chamber model to ensure no single interest group dominates.
NIST AI 800-2 establishes practices for automated benchmark evaluation of language models. We contributed observations on extending the framework to support third-party credentialing, behavioral safety evaluation, and non-technical downstream consumers of evaluation results.
The NCCoE concept paper addresses how identification, authentication, and authorization apply to AI agents. We argued that an agent's identity is incomplete without its verified behavioral safety posture, and proposed behavioral safety credentialing as an authorization gate integrated with insurance underwriting.
CAISI's RFI on security considerations for AI agents addresses adversarial cyber threats. We argued that behavioral safety constitutes a distinct and unaddressed security domain — that a technically secure agent can still cause catastrophic harm through emergent behavioral patterns.
As California's AB 566 requires all browsers to offer opt-out preference signals by January 2027, AI-driven systems will increasingly mediate the relationship between consumer privacy choices and business data practices. We addressed the verification gap — the absence of any credentialing mechanism for AI systems that receive, process, and act upon consumer opt-out preference signals.
AI systems are among the most semiconductor-intensive products in federal procurement. The proposed FAR prohibition on certain semiconductor products relies on a "reasonable inquiry" standard that is insufficient for the multi-tier, opaque AI hardware supply chain. We recommended voluntary third-party verification as a market-driven complement to self-certification, mirroring the FedRAMP and CMMC models.
Reputation risk as a supervisory tool has functioned as a barrier to financial innovation, disproportionately impacting Minority Depository Institutions and CDFIs. As autonomous AI agents increasingly require banking access, the elimination of subjective reputation assessments must be paired with objective, standards-based alternatives — independent third-party credentialing that gives banks a defensible basis for due diligence.
The Box Commons is assembling a founding board and developing its first credentialing standards. All of our standards work is published openly.
Interested in the standards process?
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