Large Language Models in Cloud Cybersecurity: Applications, Vulnerabilities, and Risk Mitigation Strategies

Large Language Models (LLMs) are emerging as the game changers in cloud cybersecurity with advanced functionality in threat detection, anomaly detection, log analysis, and automated incident response. The paper discusses the opportunities of the implementation of the LLMs in the multi-clouds, the risks, and mitigation. Based on an experimental framework of mixed-method, fine-tuned LLMs were compared with traditional machine learning participants regarding areas of performance, including accuracy, precision, recall, F1-score, AUC, detection latency and adversarial robustness. The results show that the systems designed with the help of LLM will be more accurate in detection and the mean time to detect and respond to security incidences would be significantly lower. However, additional vulnerabilities are also brought about such as threats as immediate injection, information leakage, and model contamination. Mitigation measures that are compared in the paper include prompt filtering, adversarial training, encryption and differential privacy. As part of a layered defense architecture, it has been discovered that LLMs can enhance the efficiency of cloud security, without undermining compliance, scalability and operational efficiency.