Explainable AI-Driven Cybersecurity Governance for Enterprise Digital Transformation: A Framework for Executive Decision Intelligence

Enterprise digital transformation has converted cybersecurity governance from a periodic assurance exercise into a continuous decision problem. Cloud services, application programming interfaces, digital identity, industrial connectivity, mobile work, artificial intelligence services, and third-party platforms create interdependent risks that change faster than conventional checklists and static heat maps can represent. This study develops an explainable artificial intelligence-driven cybersecurity governance and executive decision intelligence framework, designated AICG-XDI. The framework integrates security telemetry, vulnerability evidence, asset criticality, control effectiveness, external exposure, user-behavior indicators, policy compliance, zero-trust maturity, and business impact within a unified governance score. Supervised prediction, anomaly detection, local feature attribution, an explainability adequacy index, and a decision-utility function are combined so that technical risk estimates can be translated into traceable executive actions. The revised evidence base retains the manuscript's established scholarly and standards sources and incorporates 41 application-oriented TechConnect publications covering cybersecurity management, trustworthy sensing, machine learning, data fusion, predictive modelling, digital transformation, fault diagnosis, and operational decision support. A synthetic benchmark containing 25,000 enterprise observations is used to compare checklist governance, rule-based scoring, logistic regression, random forest, gradient boosting, a deep neural network, and AICG-XDI. In the controlled simulation, AICG-XDI records recall of 0.91, precision of 0.90, an F1-score of 0.905, an area under the curve of 0.96, an explainability score of 0.93, and a governance decision intelligence score of 0.88. These results demonstrate the internal behavior of the proposed artefact rather than performance in a live enterprise. The framework provides a structured basis for patch prioritization, privileged-access intervention, supplier escalation, control investment, incident reporting, and cyber-resilience planning while preserving human accountability.