The increasing digitalization of various industries has led to a surge in cybersecurity threats, necessitating advanced protection mechanisms. This study explores the integration of artificial intelligence in cybersecurity frameworks, focusing on threat detection, data privacy, secure system architectures. AI-driven models, including deep learning and reinforcement learning, have demonstrated significant potential in identifying cyber threats in real-time. Additionally, privacy preserving techniques such as federated learning and block chain technology offer robust solutions for securing sensitive data while ensuring compliance with regulatory standards. The study further examines Zero Trust Architecture as a security framework to enhance system resilience against unauthorized access. Despite these advancements, challenges such as system interoperability, computational efficiency, and scalability persist. This research identifies existing gaps in AI-powered cybersecurity and proposes an adaptive security framework that integrates threat intelligence, privacy mechanisms, real-time risk mitigation. The findings highlight the necessity of continuous advancements in AI models to address evolving cyber threats and enhance the overall digital security.