Real-Time AI-Driven Cybersecurity Analytics Dashboards for Critical-Infrastructure Protection under Saudi Vision 2030: An NCA- and SDAIA-Aligned Governance-to-Execution Framework

AI-Driven Threat Intelligence for National Cybersecurity Governance: A Framework for Adaptive Risk Detection and Policy-Aware Response

The rapid evolution of cyber threats has exposed critical limitations in traditional, rule-based cybersecurity governance models. Nation-state infrastructures increasingly face adaptive, artificial intelligence (AI)-assisted attacks that outpace static defence mechanisms and policy frameworks. This paper proposes an AI-driven threat intelligence framework designed to enhance national cybersecurity governance through adaptive risk detection, contextual analysis, and policy-aware response orchestration. The framework integrates machine learning (ML)-based anomaly detection, graphbased threat correlation, and governance-aligned decision layers to bridge the gap between technical cybersecurity operations and regulatory oversight. Unlike conventional security information and event management systems, the proposed approach emphasises explainability, institutional accountability, and alignment with national digital governance objectives. The paper presents the conceptual architecture, operational workflow, and governance implications of the framework, demonstrating how AI can support strategic cyber resilience while preserving transparency and policy compliance. The findings contribute to applied cybersecurity research by offering a scalable, governance-centric model suitable for critical infrastructure protection and national cyber defence strategies.

CRITICAL-INFRASTRUCTURE PROTECTION UNDER SAUDI VISION 2030: AN NCA-AND SDAIA-ALIGNED GOVERNANCE-TO-EXECUTION FRAMEWORK

AI-driven cybersecurity in the gulf states: governance challenges and a proposed GCC-wide ethical framework

The study proposes a novel GCC-wide AI governance framework comprising four integrated layers: regulatory (risk-based classification), technical (explainable AI methods such as SHAP/LIME, federated learning, and differential privacy), oversight, and capacity-building (workforce development and regional intelligence sharing).

Strengthening Cybersecurity and Data Governance in Nigeria’s Aviation Industry: Implications for Blockchain, Artificial Intelligence AI, and Big Data Systems

Nigeria’s aviation industry is increasingly adopting digital systems, including blockchain, artificial intelligence and big data analytics, for passenger processing, cargo tracking, predictive maintenance, operational monitoring and regulatory oversight. These technologies may improve efficiency and decision-making, but they also introduce cybersecurity and data governance risks that can affect safety, confidentiality, compliance and operational resilience. This study examined cybersecurity readiness and data governance practices associated with the use of blockchain, artificial intelligence and big data systems in Nigeria’s aviation sector. A mixed-method research design was adopted. Quantitative data were collected from aviation stakeholders using structured questionnaires, while qualitative insights were obtained from key informant interviews. The study focused on regulatory agencies, airport authorities, airline personnel, ICT managers and senior management staff involved in digital aviation systems. Out of 220 questionnaires administered, 198 were completed and returned, representing a 90% response rate. The findings showed a modest level of cybersecurity preparedness among respondents, with a grand mean of 3.00. Data governance practices were also found to be present but not fully mature, with a grand mean of 2.93. The most frequently reported challenges were inadequate cybersecurity financing, lack of experienced cybersecurity personnel, weak regulatory enforcement, legacy information systems and poor collaboration among institutions. Alignment with international cybersecurity standards was also moderate, with a grand mean of 2.92. The study concludes that stronger institutional coordination, improved funding, staff development, periodic cybersecurity assessment and sector-specific data governance frameworks are required to support secure digital transformation in Nigeria’s aviation sector.

Autonomous Decision Assurance Layer for AI-Driven Enterprise Analytics: Bridging Governance, Multi-Agent AI, and Executive Decision Intelligence in Saudi Vision 2030 Organizations

An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.

Enhancing Government Cybersecurity through the Utilization of Automated and AI-Driven Techniques to Fortify Security Information and Event Management (SIEM) Systems

A better AI-driven SIEM framework that combines machine learning-based threat detection with an automated incident response layer that follows security playbooks that have already been set up is suggested.