Regulatory bodies, including the EU, have established frameworks to ensure the safety of high-risk artificial intelligence systems before deployment. However, a significant gap exists in the certification process, as current methods struggle to provide comprehensive assessments of AI risk. To address this, researchers have proposed a statistical certification framework, which aims to provide a more rigorous and transparent evaluation of AI systems. This framework is designed to bound the uncertainty associated with AI decision-making, enabling more effective risk regulation. The proposed approach has significant implications for entities subject to regulations such as the EU AI Act, as early adoption of this framework could provide a competitive advantage in terms of compliance1. The development of this framework is crucial, as it has the potential to reshape compliance requirements and mitigate the risks associated with high-risk AI systems, so practitioners must prioritize understanding and integrating this framework to ensure the safe and responsible deployment of AI.