Artificial Intelligence for Cybersecurity: A Comprehensive Analysis of Algorithms, Frameworks, and Real-World Applications - Sajid, Saidamin; Ibrahimi, Eid Mohammad; Raoufi, Baryali (Journal of International Accounting, Taxation and Information Systems, 2025)
The rapid rise in cyber threats has necessitated the integration of Artificial Intelligence (AI) to enhance cybersecurity strategies. This study aims to examine the effectiveness of AI algorithms in detecting and mitigating cyber threats, analyze AI-driven frameworks for cybersecurity operations, and assess real-world applications and challenges in deployment. A qualitative methodology was employed through a systematic literature review of 30 peer-reviewed articles published between 2021 and 2025, sourced from academic databases such as IEEE Xplore, ScienceDirect, Springer, and Wiley Online Library. Data extraction and screening were guided by the PRISMA protocol to ensure the inclusion of high-quality, relevant studies. Results indicate that AI techniques such as neural networks, support vector machines, and deep learning are highly effective in identifying anomalies, detecting intrusions, and analyzing malware. Furthermore, AI-based cybersecurity architectures are increasingly adaptive, scalable, and integrated with real-time response systems. However, challenges remain in model explainability, data privacy, and adversarial attacks.The study concludes that while AI significantly improves cybersecurity capabilities, its deployment must be guided by ethical, legal, and operational considerations. Future research should focus on improving model transparency and developing adaptive defense mechanisms.