Model Attestation

Model attestation is a verifiable statement that a specific AI model version has the properties an organization is relying on. It can cover origin, license status, training or fine-tuning basis, safety checks, approved use cases, known limitations, evaluation results, and in stronger implementations a digital signature from the provider or an internal review pipeline. The distinction from model provenance matters: provenance explains where a model came from and how it changed, while attestation confirms specific claims about one model at one point in time. That becomes important when models move from experiments into regulated or business-critical workflows. A useful attestation can show that a model came from an approved source, passed defined tests, meets contractual and data-protection requirements, and can be traced if its behavior changes later. Without it, teams often depend on provider copy, version labels, or informal approvals. With model attestation, model selection becomes auditable evidence rather than a one-off trust decision, which helps procurement, security, compliance, and incident response work from the same facts.