Cutting-edge technology are necessary for cybersecurity to stay ahead of the ever changing landscape of cyberattacks. This industry has changed as a result of AI, which makes automated incident response, predictive analytics, and real-time threat identification possible. With an emphasis on LLMs and federated learning, this research examines recent developments in cybersecurity powered by AI. Large amounts of structured and unstructured data may be processed by LLMs like GPT-4, which improves threat intelligence and vulnerability identification. Federated learning allows for collaborative protection without disclosing sensitive data by offering privacy-preserving anomaly detection techniques across distributed platforms. The hybrid approach employs adversarial training to make the model resilient to complex attacks, federated learning for decentralized anomaly detection, and LLMs for threat intelligence. The method preserves data privacy while achieving 95% detection accuracy on benchmark datasets such as CICIDS2017 and MITRE ATT&CK. Furthermore, LLMs enhance attack pattern mitigation and prediction, resulting in a 30% decrease in false positives. This study demonstrates that federated learning and LLMs can manage the privacy, scalability, and adaptability concerns of cybersecurity. The results demonstrate that AI can significantly increase resistance to APTs. Future directions include more reliable federated optimization techniques to boost efficiency and dependability in changing cyber environments and blockchain for immutable audit trails.