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).
Abstract
Artificial intelligence (AI) presents substantial opportunities to strengthen national cybersecurity in the Gulf States while raising significant governance challenges related to algorithmic bias, privacy protection, accountability, and potential misuse. This study examines the deployment and governance of AI for cybersecurity in Qatar, Saudi Arabia, and the United Arab Emirates (UAE). It assesses the effectiveness of AI applications in threat detection and response, identifies regulatory gaps relative to international standards, and proposes a regionally tailored governance framework. Guided by Technological Governance Theory, which emphasizes multi-stakeholder collaboration and adaptive policymaking, and the Strategic Alignment Model, which evaluates the coherence between technological initiatives and national security objectives, the research employs a mixed-methods approach. Quantitative analysis draws on aggregated cyber incident data (2018–2025) from GCC cybersecurity agencies, with conservative estimates and sensitivity analyses to account for under-reporting (60–100% completeness range). Qualitative components include thematic analysis of policy documents (e.g., Qatar’s National AI Strategy and the EU AI Act), comparative case studies of facial recognition systems, predictive threat intelligence, and smart policing applications, and 12 semi-structured expert interviews. Results indicate meaningful efficiency gains, such as reduced breach response times and strong domain-specific detection rates, though these are caveated by data limitations and varying audit coverage. 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 (mandatory bias audits, human-in-the-loop requirements, and an independent GCC AI Ethics Council), and capacity-building (workforce development and regional intelligence sharing). This framework differentiates itself from the EU AI Act through enhanced focus on state sovereignty and cultural alignment, and from Singapore’s Model by incorporating stronger enforcement and data localization mechanisms for critical infrastructure. The findings contribute to the literature by offering an operational model that balances security effectiveness with ethical imperatives. The Gulf States are positioned to play a leading role in responsible AI governance, contingent on robust implementation and enforcement.
The increasing complexity of security threats demands innovative national security management approaches globally. In Nigeria, issues like terrorism, banditry, cybercrime, and attacks on critical infrastructure weaken stability and development. This study investigates the role of AI-driven governance in addressing these contemporary threats and enhancing policy responses in Nigeria. Grounded in Digital Governance Theory and Securitization Theory, the research utilized a qualitative design, conducting a qualitative literature review with secondary data sourced from peer-reviewed journals and institutional reports. The findings indicate that artificial intelligence can significantly improve intelligence gathering, predictive analytics, cybersecurity management, and evidence-based policymaking. A comparative analysis of best practices from countries like Singapore, Estonia, India, and Israel highlights that successful AI adoption requires robust digital infrastructure, effective regulatory frameworks, sustained investments, and institutional collaboration. However, the study identifies critical constraints in Nigeria, such as inadequate digital infrastructure, limited technical expertise, weak policy frameworks, institutional fragmentation, insufficient funding, and cybersecurity vulnerabilities. The study concludes that AI-driven governance can enhance Nigeria’s national security framework and responsiveness to emerging threats. Recommendations include developing a comprehensive National AI Security Strategy, increasing investments in digital infrastructure and human capital, and strengthening legal, regulatory, and international cooperation to support effective AI technologies in national security management.
Daniel Chimaobim Achinike, Ifeanyi Ositadinma Onuigbo, Juliana Onyedika Iwuno et al.· IPS Journal of Management an...· 0 citations
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.
M. M. Salem· Applied Cybersecurity &...· 0 citations
Artificial Intelligence (AI) is rapidly reshaping the structures and processes of governance and transforming the domain of cybersecurity, thereby redefining the conceptual and practical foundations of national security policy. Unlike earlier waves of digitalization, AI introduces algorithmic decision-making, predictive analytics, and autonomous systems into core governance functions, including policy formulation, service delivery, surveillance, and cyber defense. This paper examines the intersection of AI, governance, and cybersecurity from an interdisciplinary perspective grounded in political science, governance studies, and national security studies. It argues that AI constitutes not merely a technological instrument but a transformative governance force that reconfigures state capacity, sovereignty, power relations, and security practices. Drawing upon classical and contemporary literature from Indian and international scholars, the study adopts a qualitative, analytical methodology based on documentary analysis of academic texts, policy documents, and national cybersecurity strategies. The paper contends that national security in the digital age must be reconceptualized beyond military paradigms to include digital infrastructure resilience, information integrity, algorithmic accountability, and institutional legitimacy. It concludes that effective national security policy requires integrated governance frameworks that embed AI within ethical norms, legal safeguards, and democratic oversight mechanisms.
Vinay Kumar· Dynamics of Public Administr...· 0 citations
A novel, unified governance framework centred on digital trust is proposed that distinctly integrates the AI Trust Framework and Maturity Model (AI TMM), the Tiered Ethical Cybersecurity Model (TECM), and privacy preserving technologies such as federated learning to operationalize ethics by design.
Muhammad Faris bin Nordin, Muhammad Din bin Khalid, Normal Mat Jusoh· International journal of res...· 0 citations
A seven-domain typology identifying ten specific AI-driven cyber governance failure causes grounded in public sector institutional analysis is proposed and speed asymmetry is introduced as a named structural construct with a specified mechanism.
Muflihah Salahuddin, James Rooney, Fida Hasan· arXiv.org· 0 citations
This review synthesizes cross-sector evidence to identify structural, operational, and technological barriers to harmonization, while proposing an integrative governance perspective grounded in recent empirical and policy literature.
William Asare Yirenkyi, G. Apaflo, Matilda Konotey et al.· Magna Scientia Advanced Rese...· 0 citations
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