• 1Dr. Rajinder Kumar
• Mr. Sahil Sharma
• Ms.Sukhwinder Kaur
• Mr Ranveer Singh

In the age of fast-moving cyber threats, rule-based and signature-based cybersecurity technologies are no longer sufficient. Artificial intelligence, based on the latest technologies, promises to enhance threat detection and defense capabilities in an adaptive, intelligent, and scalable way. This paper presents a structured study of the integration of AI in cybersecurity, with a special focus on a combined case study of the platforms deployed by organizations such as Aviso and Darktrace. Through a literature review of AI methods in cybersecurity, we develop a methodology for integrating AI-driven detection into enterprise environments. We then examine how the two organisations implemented AI capabilities in threat detection and defence, analyse results, and discuss the broader implications. Key findings indicate that AI-powered systems succeed in detecting novel threats and reducing time to remediation, but also raise challenges around interpretability, data quality, false alerts, and organisational embedding. Finally, we conclude with implications for practice and a view toward future research directions, including explainable AI, federated learning, and operational integration of AI cyber-defence.

Artificial intelligence, cybersecurity, threat detection, machine learning, enterprise defense, case study.

"Smart Defenders: An Empirical Analysis of Adversary System Resilience in Cybersecurity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.f330-f340, November-2025, Available :http://www.jetir.org/papers/JETIR2511560.pdf

"Smart Defenders: An Empirical Analysis of Adversary System Resilience in Cybersecurity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppf330-f340, November-2025, Available at : http://www.jetir.org/papers/JETIR2511560.pdf