AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection: Integrating Zero Trust, Digital Resilience, and Institutional Preparedness for Adaptive Cyber Defense

EXECUTIVE SUMMARY Critical infrastructure sectors—including energy, healthcare, transportation, telecommunications, finance, water systems, and government services—are increasingly dependent on interconnected digital technologies. While these technologies improve efficiency, innovation, and service delivery, they simultaneously expand the attack surface available to cyber adversaries. This study proposes an AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection that integrates Artificial Intelligence (AI), Zero Trust governance, cyber risk scoring, operational resilience, and institutional preparedness into a unified governance architecture. The framework addresses a persistent challenge within cybersecurity practice: the disconnect between technical threat detection and governance-level cyber risk decision-making. Drawing upon established cybersecurity frameworks including NIST Cybersecurity Framework 2.0, NIST Zero Trust Architecture, ISO/IEC 27001:2022, ENISA guidance, and CISA Cybersecurity Performance Goals, the study develops a seven-layer governance model that supports critical asset protection, AI-assisted threat detection, resilience planning, executive oversight, and continuous improvement. The proposed Cyber Risk Score (CRS) model further enables organizations to prioritize cyber risks according to threat probability, asset criticality, exposure severity, business impact, and recovery readiness. The framework contributes to cybersecurity governance research by providing a practical and scalable approach suitable for both developed and developing economies. It emphasizes that effective cybersecurity is no longer solely a technical challenge but a governance, resilience, and institutional preparedness challenge requiring coordinated leadership across technical, operational, and executive domains. Keywords: Cybersecurity Governance, Artificial Intelligence, Critical Infrastructure Protection, Digital Resilience, Zero Trust, Cyber Risk Management, Institutional Preparedness, Cyber Resilience.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-29
DOI
https://doi.org/10.5281/zenodo.20446325
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
preprint
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preprint

AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection: Integrating Zero Trust, Digital Resilience, and Institutional Preparedness for Adaptive Cyber Defense

Vincent Chinedu Johnson
Zenodo (CERN European Organization for Nuclear Research)
Infrastructure Resilience and Vulnerability Analysis
preprint

AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection: Integrating Zero Trust, Digital Resilience, and Institutional Preparedness for Adaptive Cyber Defense

Vincent Chinedu Johnson
preprint en

Abstract

EXECUTIVE SUMMARY Critical infrastructure sectors—including energy, healthcare, transportation, telecommunications, finance, water systems, and government services—are increasingly dependent on interconnected digital technologies. While these technologies improve efficiency, innovation, and service delivery, they simultaneously expand the attack surface available to cyber adversaries. This study proposes an AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection that integrates Artificial Intelligence (AI), Zero Trust governance, cyber risk scoring, operational resilience, and institutional preparedness into a unified governance architecture. The framework addresses a persistent challenge within cybersecurity practice: the disconnect between technical threat detection and governance-level cyber risk decision-making. Drawing upon established cybersecurity frameworks including NIST Cybersecurity Framework 2.0, NIST Zero Trust Architecture, ISO/IEC 27001:2022, ENISA guidance, and CISA Cybersecurity Performance Goals, the study develops a seven-layer governance model that supports critical asset protection, AI-assisted threat detection, resilience planning, executive oversight, and continuous improvement. The proposed Cyber Risk Score (CRS) model further enables organizations to prioritize cyber risks according to threat probability, asset criticality, exposure severity, business impact, and recovery readiness. The framework contributes to cybersecurity governance research by providing a practical and scalable approach suitable for both developed and developing economies. It emphasizes that effective cybersecurity is no longer solely a technical challenge but a governance, resilience, and institutional preparedness challenge requiring coordinated leadership across technical, operational, and executive domains. Keywords: Cybersecurity Governance, Artificial Intelligence, Critical Infrastructure Protection, Digital Resilience, Zero Trust, Cyber Risk Management, Institutional Preparedness, Cyber Resilience.

Zenodo (CERN European Organization for Nuclear Research)
European School of Management and Technology (DE)
Industry, innovation and infrastructure
Infrastructure Resilience and Vulnerability Analysis
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