Adaptive AI Governance as a Lifecycle Control System: From Static Compliance to Continuous Oversight

This policy brief introduces a new way of thinking about artificial intelligence governance by focusing on a lifecycle-based approach. Instead of seeing governance as a series of one-time compliance checks, it shifts to viewing it as an ongoing process that continuously monitors and manages AI systems. The framework we propose, the Adaptive Lifecycle AI Governance Framework (ALAGF), demonstrates how to implement governance measures at the beginning of the AI development process, use real-time data to track behavior, and establish clear protocols for escalating issues. This integrated approach helps us identify and address problems such as bias, misinformation, and errors throughout the lifecycle of an AI system. Rather than relying on post hoc reports, our approach emphasizes the importance of measurable system behaviors, ensuring that our operational practices align with international standards such as the NIST AI Risk Management Framework and ISO/IEC 42001. By viewing AI governance as an engineering challenge that involves constant monitoring and adjustment, this paper offers a practical model for policymakers, practitioners, and researchers. It aims to promote accountability, regulatory compliance, and the responsible deployment of trustworthy AI systems.