Cyber threats evolve rapidly, making traditionaldefense mechanisms insufficient. Machine Learning (ML) introduces dynamic,adaptive security by analyzing vast amounts of network data to detect anomaliesand predict potential attacks before they occur. ML models learn typicalpatterns of user behavior and flag deviations that could signal breaches orinsider threats. Automated threat hunting reduces response times and mitigatesdamage. However, the cybersecurity domain also faces unique challenges withadversarial attacks targeting AI systems themselves by manipulating inputs tofool models. This blog reviews how ML enhances intrusion detection, fraudprevention, and vulnerability management, alongside the risks and futuredirections for AI-driven cybersecurity. It explains key concepts likesupervised and unsupervised learning in security and offers examples ofindustry-leading solutions leveraging AI.