Aug 2026· Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023· 0 citations· 29 references
Abstract
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.
It is concluded that AI has become an indispensable component of modern cybersecurity strategies and will play a critical role in safeguarding digital infrastructure against emerging cyber threats.
Shaurya Gupta· Innovative Research Thoughts· 0 citations
Modern organizations face an expanding threat landscape of sophisticated malware, automated
intrusions, and large-scale data breaches that outpace traditional signature-based defenses. This
paper examines the role of artificial intelligence (AI) in strengthening modern cybersecurity
systems and provides a structured and comprehensive synthesis of the field, covering the
foundations and taxonomy of AI techniques, their application to proactive detection, incident
response, and security operations across networks, endpoints, and cloud, and the benefits,
challenges, sector specific applications, evaluation methods, ethical and workforce
considerations, and the emerging role of generative artificial intelligence. The review finds that
artificial intelligence enables proactive threat detection, anomaly identification, and the
automation of analysis and response at a scale beyond human capacity, yet its effectiveness is
constrained by adversarial machine learning, data quality and drift, false positives, opacity, and
the dual use of generative models by attackers. It concludes that the most effective and responsible
deployments integrate artificial intelligence with established controls, ground it in sound data and
governance, and preserve human oversight, and it offers recommendations and identifies open
challenges for researchers, practitioners, and policymakers.
N. Hussain· WORLD JOURNAL OF INNOVATION...· 0 citations
The rapid expansion of cloud computing infrastructures has fundamentally transformed how organizations manage
and deploy digital services, simultaneously introducing a complex and evolving attack surface that traditional security
mechanisms fail to adequately address. This survey examines the convergence of artificial intelligence (AI) and autonomous
cybersecurity with a focus on cloud environments. We systematically review thirteen recent papers spanning five core research
themes: AI-driven threat detection and classification, explainable AI (XAI) for cybersecurity transparency, autonomous response
and mitigation strategies, real-time cyber threat attribution, and AI-enhanced education for cybersecurity workforce
development. Our analysis highlights the state-of-the-art techniques including Graph Neural Networks (GNNs), transformerbased attention mechanisms, Federated Deep Learning (FDL), reinforcement learning, and multi-modal data fusion, all applied
to the challenge of building self-healing, autonomous cloud defense systems. We further discuss persistent challenges such as
dataset quality, model interpretability, adversarial robustness, and the gap between academic research and real-world
deployment. This survey provides a structured synthesis of the current state of the art, identifying key research directions for the
next generation of intelligent, autonomous cloud security systems
Mohammed Adeen Khurshid, Mulla, Mohammed Zuber Mulla, Mohammed Aziz A Khazi et al.· International Journal for Re...· 0 citations
This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems
R. K. Saidala, Amirkhan Pashayev, Tofig Hasanov· Journal of Computational Sci...· 0 citations
The growing complexity, size, and dynamism of cyber threats have revealed inherent weaknesses of the traditional, static cybersecurity models. Attackers nowadays take advantage of artificial intelligence, automation, and adversarial learning methods to avoid detection, create new variants of attacks, and maintain long-term intrusions. In response, cybersecurity research has turned to self-evolving cyber defense systems that can engage in constant learning, autonomous decision-making, and co-evolution with intelligent attackers. This review includes a systematic synthesis of the studies on self-evolving cyber defense with a focus on the merging of artificial intelligence, autonomy, and adversarial learning. We discuss the evolution of cyber threats, machine learning and deep learning approaches for adaptive threat detection, as well as autonomous defense systems enabled by reinforcement learning and multi-agent systems. The review also explores the adversarial machine learning as a source of emerging threats and a powerful defense foundation with focus on the co-evolution of the attackers and the defenders. In addition to algorithmic views, the paper has provided an overview of system architectures, evaluation measures, ethics and legal aspects, and real-life implementation of industrial applications in critical infrastructures, military systems, financial services, smart cities, and cyber-physical environments. The major issues concerning scalability, resistance to adaptive opponents, lack of information, and the interaction of humans and AI are addressed. Lastly, the review presents directions of future research, such as entirely autonomous defense ecosystems, hybrid neuro-symbolic systems, quantum-resilient AI security, and intelligence sharing across domains. This work brings together dispersed research in various fields to offer a reference and roadmap on how to progress to the next generation of self-evolving cyber defense systems.
Babatunde AbdulAzeez Alli, Taoheed Abiodun Yusuf, Jefferson Ederhion et al.· Journal of Computer Virology...· 0 citations
The rapid expansion of digital infrastructures, cloud ecosystems, Internet of Things (IoT) environments, and intelligent enterprise platforms has significantly increased the complexity and frequency of cybersecurity threats. Traditional security mechanisms based on static rules and signature-based detection approaches are increasingly insufficient against sophisticated attacks involving zero-day exploits, advanced persistent threats, automated malware, and coordinated intrusion campaigns. This research paper presents a comprehensive analysis of AI-driven cybersecurity frameworks designed for real-time intrusion detection and threat intelligence generation. The study explores the integration of artificial intelligence (AI), machine learning (ML), deep learning, behavioral analytics, automation, and intelligent decision-making mechanisms for developing adaptive cybersecurity architectures. A research-oriented review methodology is adopted by synthesizing existing contributions from the provided literature, focusing on AI-enabled fraud detection, secure DevOps, zero-trust security, digital twin environments, distributed computing, privacy-preserving models, and intelligent risk assessment frameworks. The proposed analytical framework examines key components including real-time data acquisition, AI-based anomaly detection, threat intelligence processing, automated response orchestration, and continuous security optimization. Findings indicate that AI-driven cybersecurity architectures enhance detection accuracy, reduce response latency, and improve resilience against evolving cyber threats. However, challenges related to explainability, adversarial AI attacks, data privacy, computational requirements, and regulatory compliance remain significant barriers to large-scale adoption. The study contributes a structured understanding of how AI technologies can transform cybersecurity operations from reactive defense mechanisms into proactive, predictive, and autonomous security ecosystems.
Dilshan Jayawardena, Dr. Kavindi Perera· International Journal of Com...· 0 citations