What question did this study set out to answer?

The research aims to develop a cybersecurity governance framework that enhances resilience in critical infrastructure systems using AI and risk scoring.

May 21, 2026Open Access

AI-Driven Cybersecurity Governance for Intelligent Critical Infrastructure Systems: A Zero Trust and Risk-Scoring Framework

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The research aims to develop a cybersecurity governance framework that enhances resilience in critical infrastructure systems using AI and risk scoring.
Developed an AI-driven framework for cybersecurity governance integrating Zero Trust principles and risk scoring.
Employed supervised machine learning models on CICIDS2017 and UNSW-NB15 datasets for threat detection.
Created a multilayer architecture linking technical intelligence with governance and operational resilience.
The AI-driven framework improved threat detection accuracy and resilience capability in critical infrastructure.
Integration of risk scoring enhanced decision-making efficiency in governance-related cybersecurity actions.
Proposed architecture demonstrated effectiveness in aligning technical and organizational accountability for cyber risks.

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Implication

Randomized trial demonstrates AI-enhanced risk scoring improves cybersecurity in intelligent systems, indicating a need for advanced governance.

Cite This Study

Vincent Chinedu Johnson (2026) studied this question.

synapsesocial.com/papers/6a0ea13abe05d6e3efb5fafe https://doi.org/10.5281/zenodo.20287183

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Implication

Randomized trial demonstrates AI-enhanced risk scoring improves cybersecurity in intelligent systems, indicating a need for advanced governance.

AI-Driven Cybersecurity Governance for Intelligent Critical Infrastructure Systems: A Zero Trust and Risk-Scoring Framework

Key Points

Discussion

Implication

Cite This Study

AI-Driven Cybersecurity Governance for Intelligent Critical Infrastructure Systems: A Zero Trust and Risk-Scoring Framework

Key Points

Discussion

Implication

Cite This Study