Aug 2026· American Journal Of Applied Science And Technology· Vol 06, pp. 40-64· 0 citations
TL;DR
The findings suggest that Palestine can enhance its cybersecurity resilience by adopting a phased and adaptive strategy that leverages human capital, strengthens institutional coordination, and aligns with international cybersecurity standards.
Abstract
The rapid advancement of artificial intelligence (AI) has significantly transformed the cybersecurity landscape, introducing both enhanced defensive mechanisms and increasingly sophisticated cyber threats. Nations worldwide are integrating AI into their cybersecurity strategies to improve threat detection, automate responses, and strengthen resilience against cyberattacks. However, the dual-use nature of AI also enables adversaries to develop advanced attack techniques, including intelligent malware, automated phishing, and deepfake-based disinformation campaigns [1], [2].
In Palestine, the need for a comprehensive national cybersecurity strategy is becoming increasingly urgent due to ongoing digital transformation across governmental, economic, and social sectors. Despite this progress, the Palestinian cybersecurity ecosystem faces critical challenges, including fragmented institutional governance, limited technical infrastructure, insufficient legal frameworks, and constraints related to digital sovereignty [3].
This research aims to develop a strategic framework for a national cybersecurity strategy in Palestine that integrates AI technologies while addressing local constraints and global best practices. The study adopts a qualitative analytical approach based on literature review, comparative analysis of international models, and evaluation of existing Palestinian policies. The proposed framework—Palestinian Cybersecurity Strategy Framework (PCSF)—is built upon five key pillars: governance, legal and regulatory frameworks, capacity building, technological infrastructure, and international cooperation, with AI integration as a cross-cutting component.
The findings suggest that Palestine can enhance its cybersecurity resilience by adopting a phased and adaptive strategy that leverages human capital, strengthens institutional coordination, and aligns with international cybersecurity standards. The study contributes to both academic research and policy development by providing a context-specific model for cybersecurity strategy in environments characterized by political and technological constraints.
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 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 (AI) has emerged as a transformative force in cybersecurity, offering capabilities that extend far beyond the static, rule-based defenses of the past. Machine learning, deep learning, and natural language processing techniques are increasingly embedded in intrusion detection systems, threat intelligence platforms, and automated incident response tools, enabling organizations to identify and neutralize threats with greater speed and precision. However, the same interconnectedness that drives digital transformation—spanning IoT ecosystems, cloud infrastructures, and 5G networks—has also expanded the attack surface available to malicious actors, giving rise to increasingly sophisticated, adaptive, and often AI-enabled threats such as adversarial machine learning attacks, deepfake-driven social engineering, and automated supply chain exploits. This paper examines the dual role of AI as both a defensive asset and a potential vector of risk within modern cybersecurity ecosystems. Drawing on a review of existing AI-driven security solutions, comparative analysis of AI-based versus traditional defense mechanisms, and case study evaluation, the study assesses the effectiveness, limitations, and ethical implications of AI integration in cyber defense. Findings indicate that while AI substantially improves threat detection accuracy and response times, challenges related to explainability, adversarial vulnerability, and regulatory oversight remain significant barriers to widespread adoption. The paper concludes with practical recommendations for organizations and policymakers seeking to harness AI's defensive potential while mitigating its associated risks, emphasizing the need for explainable AI frameworks, human-AI collaboration, and adaptive governance structures in an increasingly interconnected digital age.
Nicolas Guzman Camacho· Journal of Artificial Intell...· 0 citations
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).
Mustafa Osman I. Elamin· Discover Artificial Intellig...· 0 citations
Nigeria faces escalating cybersecurity challenges, recording an average of 4,200 weekly cyberattacks per
organization-the highest in Africa and 60% above the global average [1]. The weaponization of Artificial Intelligence (AI)
by threat actors, including terrorist organizations using frontier AI models for operational planning and tactical decisionmaking [2], has fundamentally transformed the threat landscape. Traditional security frameworks, designed for static,
pattern-based threats, are increasingly inadequate against AI-driven intrusions such as automated phishing, identity
exploitation, and multi-vector ransomware [1]. This paper presents the design, implementation, and evaluation of an AIassisted cybersecurity intelligence platform integrating the DeepSeek API to detect, analyze, and mitigate digital threats in
the Nigerian context. The platform employs a Python-based architecture that ingests threat indicators, classifies and explains
attacks using DeepSeek's natural language processing capabilities, and delivers actionable mitigation recommendations with
confidence scoring. The system is made available as open-source code on GitHub and deployed via Streamlit Cloud for
practical adoption.
Orji, Cyrus Ebere, Paul Nosike, O. C. et al.· International Journal of Inn...· 0 citations
This review provides a novel synthesis of recent Large Language Model applications in threat hunting and identifies critical research gaps, and presents a refined perspective on the practical implementation and future trajectory of these technologies.
D. Mohammed, Shahram Jamali· 0 citations
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