Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various industries. As AI adoption grows, so do the ethical and regulatory concerns surrounding its deployment. To address these challenges, the European Union (EU) introduced the EU Act on Artificial Intelligence (AIA) in 2021, aiming to foster responsible and ethical AI development. However, the AIA’s risk assessment framework has been subject to scrutiny, and there is a need for a more comprehensive and principled approach. In this research study, we conducted an extensive literature study of global AI regulations, standards, and academic research on risk assessment to identify best practices. Building upon this knowledge, we devised a quantitative risk assessment model grounded in six fundamental ethical principles: transparency, accountability, justice, reliability, sustainability, and privacy. By evaluating AI systems based on these principles, our model provides a holistic and standardized approach to assess the potential risks and ethical implications of AI deployments. Our analysis revealed key challenges in the EU AIA’s risk assessment process, including the lack of a clear definition of risk categories for broad AI systems and the need for enhanced transparency and accountability in the assessment process. To address these challenges, we propose the incorporation of ethical principles as criteria for assessment, enabling a more nuanced evaluation of AI system risks. Furthermore, our study recommends an active participation and compliance mechanism from AI system providers and deployers in the risk assessment process. This collaborative approach fosters a culture of responsible AI development while avoiding overregulation and promoting ease of doing business. The findings from this research offer valuable insights for enhancing the risk assessment framework within the EU AIA and provide a quantitative model that aligns with global best practices. This study contributes to the growing body of literature on AI regulation and sets the stage for future research to study the practical utility of the proposed model in organizational setups and current and future AI systems.