AI and machine learning in cybersecurity: Practices, opportunities and challenges

This paper assesses the role of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, to counter the emerging threats like zero-day exploits and ransomware. Legacy rule-based systems, bound to identifying recognized attack patterns, fail against contemporary adversarial strategies. Correspondingly, the evolution of AI techniques in cybersecurity has been presented to illustrate how the hybrid architectures attain better malware detection and less false positive. Examples from the industry has been included to showcase the role of AI in network defense, endpoint security, and cloud security. Along with these practices, challenges including the adversarial attacks and skills gaps in the workforce have also been explored to support the implementation of AI-based systems. Highlighting the growing security needs, the technological issues, ethical and privacy issues, policy implications as well as the regulatory compliances have been discussed from an international perspective. New trends such as quantum-resistant cryptography and self-healing systems are projected onto next-generation cybersecurity planning. This synthesis combines the technical deployments with policy implications and provides actionable recommendations for adaptive defense systems.