This study proposes an AI-driven cybersecurity governance framework designed to strengthen resilience across intelligent critical infrastructure systems through adaptive risk scoring, Zero Trust principles, machine learning-based threat detection, governance orchestration, and continuous feedback mechanisms. The framework integrates artificial intelligence, cybersecurity governance, and cyber resilience principles to support accountable and policy-aligned protection of interconnected cyber-physical environments including healthcare, energy, telecommunications, transportation, smart cities, and industrial systems. Using supervised machine learning models evaluated on the CICIDS2017 and UNSW-NB15 datasets, the study demonstrates how AI-driven detection capabilities can be integrated into governance-based cybersecurity decision-making. The proposed multilayer architecture connects technical intelligence with organisational accountability, auditability, escalation procedures, and operational resilience. The paper contributes to emerging discussions surrounding AI governance, intelligent systems security, critical infrastructure resilience, digital trust, and governance-integrated cybersecurity architectures within modern interconnected environments.
Vincent Chinedu Johnson (2026) studied this question.