Artificial Intelligence (AI) is revolutionizing the cybersecurity landscape by enabling systems to detect, prevent, and respond to threats in real time. Traditional rule-based security methods struggle to keep up with advanced persistent threats, zero-day exploits, and rapidly evolving malware. AI addresses these challenges through machine learning, deep learning, and behavioral analytics to identify anomalies, classify threats, and automate incident response. Applications include intrusion detection systems, fraud detection, user behavior analytics, and malware classification. This proposal explores the integration of AI into modern security frameworks, emphasizing real-time threat intelligence, reduced false positives, and autonomous decision-making. It also highlights key challenges, such as data privacy, adversarial attacks, and the need for transparency in AI models. By combining data-driven insights with adaptive learning, AI has the potential to transform cybersecurity from a reactive process into a proactive and resilient defense strategy.