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Research Paper | Computer Science | Volume 15 Issue 7, July 2026 | Pages: 933 - 946 | India
Emerging Artificial Intelligence Models in Cybersecurity: A Systematic Analysis of Applications, Challenges and Future Directions
Abstract: The increasing sophistication of cyber threats has made securing digital infrastructures, cloud environments, critical systems, and Internet of Things (IoT) networks more challenging. Traditional cybersecurity approaches often struggle to detect advanced attacks and adapt to evolving threat landscapes. Consequently, Artificial Intelligence (AI) has emerged as a powerful enabler of intelligent threat detection, prediction, analysis, and automated response. This study presents a systematic analysis of emerging AI models in cybersecurity, including machine learning, deep learning, transformer-based architectures, Large Language Models (LLMs), generative AI, reinforcement learning, federated learning, explainable AI, and hybrid neuro-symbolic approaches. Using a PRISMA-based methodology, relevant studies were analyzed to evaluate their applications, strengths, limitations, and performance across domains such as intrusion detection, malware analysis, phishing detection, threat intelligence, vulnerability assessment, cloud and IoT security, digital forensics, incident response, and critical infrastructure protection. The analysis further examines AI-driven decision-making techniques, including Multi-Criteria Decision-Making (MCDM) methods and fuzzy logic for cybersecurity risk assessment and threat prioritization. A comparative analysis is conducted based on performance, explainability, scalability, computational efficiency, and trustworthiness. Key challenges, including adversarial attacks, data quality issues, model transparency, privacy concerns, regulatory compliance, and human?AI collaboration, are also discussed. Based on identified research gaps, an AI?Fuzzy Cybersecurity Decision-Making Framework is proposed to enhance cybersecurity governance and operational decision support. Finally, the study highlights future directions such as explainable AI, generative cyber defense, autonomous security operations centers, privacy-preserving federated learning, quantum-AI security, and human-centered security platforms, providing valuable insights for researchers, practitioners, and policymakers.
Keywords: Artificial Intelligence, Cybersecurity, Large Language Models, Fuzzy Logic, Multi-Criteria Decision-Making
How to Cite?: Anil Kumar Srivastava, Atul Verma, Rajeev Kumar, "Emerging Artificial Intelligence Models in Cybersecurity: A Systematic Analysis of Applications, Challenges and Future Directions", Volume 15 Issue 7, July 2026, International Journal of Science and Research (IJSR), Pages: 933-946, https://www.ijsr.net/getabstract.php?paperid=SR26711182324, DOI: https://dx.doi.org/10.21275/SR26711182324