A Comprehensive Review of Artificial Intelligence in Cybersecurity and Barriers to Its Adoption

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in modern cybersecurity, enhancing threat detection, predictive defense mechanisms, and adaptive response strategies. This review paper explores the integration of AI in cybersecurity systems, its key applications, and the barriers inhibiting large-scale adoption. By analyzing recent studies and industry practices, this work identifies both the technological potential and organizational challenges that influence AI-driven cybersecurity implementations. The paper concludes with recommendations for overcoming these barriers and future research directions to ensure secure and ethical AI deployment.

Introduction

Artificial Intelligence (AI) has significantly transformed digital ecosystems by automating tasks, improving data-driven decisions, and strengthening cybersecurity defenses. In cybersecurity, AI enhances threat detection and response through capabilities like anomaly detection, pattern recognition, and predictive analytics. Research shows that machine learning and deep learning outperform traditional security systems and improve intrusion detection. However, threats are evolving as attackers also use AI, creating an emerging "AI vs AI" landscape.

AI’s key contributions to cybersecurity include real-time monitoring, intelligent analytics, and advanced tools for malware detection, phishing prevention, and adaptive network defense. Despite these benefits, adoption faces challenges such as high costs, technical complexity, data privacy issues, limited skilled talent, ethical concerns, and risks from adversarial AI.

The effectiveness of AI in cybersecurity varies among organizations depending on digital maturity, governance, and workforce readiness. Future efforts should prioritize explainable AI to enhance trust, collaboration among stakeholders to advance secure AI development, and lightweight models for real-time edge security.

Conclusion

This review highlights that AI offers immense potential to revolutionize cybersecurity through automation, prediction, and adaptive learning. However, barriers such as cost, expertise shortage, and ethical dilemmas must be addressed to realize its full potential. A balanced approach involving transparency, collaboration, and standardized policies will ensure the responsible adoption of AI-driven cybersecurity solutions.

References

[1] Singh, R., et al., “AI-Powered Threat Detection Systems,” IEEE Access, 2022. [2] Kaur, P., and Sharma, N., “Machine Learning in Intrusion Detection Systems,” IJCA, 2023. [3] Malik, S., and Kumar, A., “Challenges of AI in Cyber Defense,” IJCS, 2024. [4] Das, R., “Barriers to AI Adoption in Security Systems,” Journal of Information Security, 2023. [5] Chen, L., “Explainable AI in Cybersecurity: A Review,” Elsevier, 2024.

Copyright

Copyright © 2025 Siddhesh Rajendra Salunkhe, Kevin Harry Agashe, Prof. Mukul Jagtap, Prof. Nagnath Dolare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.