The Role of Artificial Intelligence in Strengthening UAE Cyber Resilience: A Scoping Literature Review

Evolving Cyber Threat Intelligence: A Systematic Review and Comparative Analysis

To improve cybersecurity across industries, Cyber Threat Intelligence (CTI) is becoming increasingly crucial. This systematic review explores how CTI practices are evolving in response to advancements in Artificial Intelligence (AI), particularly in the context of Large Language Models (LLMs). We examined 61 peer-reviewed studies using the PRISMA methodology, which demonstrates a strict selection procedure founded on specified inclusion, exclusion, and quality standards. This approach aligns with the scope of similar systematic reviews in the field of cyber threat intelligence. The review provides a comparative synthesis of CTI research capabilities across threat detection and prediction, attribution, forecasting, and automated reporting. We classify these approaches into three categories: conventional methods, those enhanced by AI and Machine Learning, and those based on LLMs. Our findings indicate that LLMs offer significant advantages in contextual reasoning, processing unstructured threat intelligence, and generating actionable mitigation plans. However, challenges such as model explainability, data privacy, system interoperability, and standardization impede their integration into operational environments. In addition to highlighting the potential and practical limitations of LLMs in CTI, this study identifies research gaps and proposes methods to create scalable, secure, and flexible CTI systems that support real-time cyber defense.

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

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 AI Resilience Gap: Bringing Artificial Intelligence Inside the Operational Resilience Perimeter

The rapid adoption of artificial intelligence across regulated firms has produced an extensive governance response oriented around trustworthiness: the EU AI Act, ISO IEC 42001, the NIST AI Risk Management Framework, and the United Kingdom's principles-based approach all address safety, fairness, transparency, and model risk. That response is necessary but incomplete. It does not, on its own, address operational resilience: the continuity of important business services under severe but plausible disruption, the substitutability of AI components, and the concentration of dependency on the small number of firms that supply frontier models. This paper argues that AI adoption creates a resilience obligation that is distinct from, and inadequately covered by, the trustworthy AI stack, and that United Kingdom financial authorities are already closing this gap through the Financial Policy Committee's systemic analysis, the Critical Third Parties regime, and the May 2026 joint statement on frontier AI and cyber resilience. We map the two regulatory logics, identify the structural gap between them, and propose the AI Resilience Framework: a regime-agnostic method for bringing AI dependencies inside the operational resilience perimeter through dependency mapping, a criticality-substitutability tiering, the extension of impact tolerances to AI-specific failure modes, an explicit fallback doctrine, and provider level concentration management. The framework gives chief information security officers, security architects, and boards an actionable route from AI governance policy to demonstrable resilience. This work extends a companion analysis of the United Kingdom cyber resilience regulatory stack into the artificial intelligence dimension.

Unveiling the Frontiers of Cyber Threat Hunting: A Comprehensive Review of LLM-Based Approaches

Artificial Intelligence for Cybersecurity: A Scoping Survey of Paradigms, Applications, and Emerging Trends

A structured taxonomy is proposed to organize various dimensions of AI-driven cybersecurity; review them critically; and finally, discuss key challenges, open problems, and emerging trends.

Artificial Intelligence Capabilities and the Development of a Smart and Sustainable Auditing Ecosystem: The Moderating Role of Cyber Forensic Accounting Intelligence

This study examines how Artificial Intelligence Capabilities (AIC) contribute to the development of a Smart and Sustainable Auditing Ecosystem (SSAE) within public sector organizations (PSOs). The study also investigates the moderating role of Cyber Forensic Accounting Intelligence (CFAI) in strengthening this relationship. Data were collected from employees working in Vietnamese PSOs using a structured questionnaire survey. The model was examined through Covariance-Based Structural Equation Modeling using IBM AMOS 28. The findings indicate that AIC significantly supports the development of SSAE. Moreover, CFAI strengthens the influence of AIC on SSAE, suggesting that accountants’ cyber forensic competencies enhance the effectiveness of AI-enabled auditing systems. These results provide implications for policymakers, auditing authorities, and PSOs seeking to modernize auditing practices. Integrating AI technologies with cyber forensic expertise can facilitate more transparent, data-driven, and sustainable auditing systems that better respond to the challenges of digital governance.