A Unified Artificial Intelligence Governance and Reliability Engineering Framework for Secure and Autonomous Software-Intensive and Cyber-Physical Systems

Abstract

Regulators have published high-level AI risk frameworks to guide trustworthy AI development and deployment across sectors. The European Union has adopted a risk-based AI regulation that treats many AI components in cyber-physical systems as high-risk technologies requiring strong assurance. Recent work on responsible AI systems emphasizes domain definition, trustworthy design and governance, underscoring the need for traceable controls across the lifecycle. Clinical studies of AI-enabled healthcare show that model decisions directly affect real-world safety and quality of care in software-intensive environments. Systems-theoretic safety engineering demonstrates that accidents in complex socio-technical systems often arise from inadequate control structures rather than isolated component failures. Economic analyses of software automation indicate that organizations will sustain governance and reliability investments only when they deliver measurable time and cost savings. This paper proposes a Unified Artificial Intelligence Governance and Reliability Engineering (AIGRE) framework that integrates governance structures, reliability and safety engineering practices, data and ML lifecycle controls, cybersecurity mechanisms, and decision-intelligence feedback loops into a single architectural view. The framework targets software-intensive and cyber-physical systems that embed learning-enabled components, providing a methodology for mapping regulatory and organizational objectives to concrete architectural decisions, lifecycle activities, and runtime indicators. Illustrative scenarios in clinical decision-support and smart infrastructure show how AIGRE can be instantiated to provide traceable links from policy objectives to operational metrics.