Authors - Deepak sharma, Pankajkumar Anawade, Anurag Luharia, Gaurav Mishra Abstract - The rapid digital transformation of modern society has significantly increased the complexity of network infrastructures and the sophistication of cyber threats. Traditional rule-based and signature-based security systems are increasingly ineffective against advanced persistent threats, zero-day vulnera bilities, and AI-driven cyberattacks. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies that enhance net work security through intelligent threat detection, automated response, and pre dictive analytics. However, the integration of AI and ML also introduces new vulnerabilities, including adversarial attacks, model poisoning, privacy con cerns, and algorithmic bias. This paper critically examines the evolution of net work security through AI and ML, analyzing both the technological advance ments and the emerging risks associated with their deployment. The study ar gues that while AI-driven security systems represent a significant improvement over traditional mechanisms, careful governance, transparency, and robust model protection are essential to mitigate new threats introduced by intelligent systems.
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