Latest research in Deep Learning Solutions for Cloud Security focuses on employing advanced deep neural network architectures to enhance threat detection, anomaly recognition, and proactive defense mechanisms within cloud environments. Studies highlight the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, and autoencoders for detecting intrusions, identifying malicious activities, and predicting potential security breaches in real-time. Research also emphasizes hybrid and ensemble deep learning approaches integrated with intrusion detection systems, access control mechanisms, and encryption frameworks to improve accuracy, scalability, and response times in dynamic multi-tenant cloud infrastructures. These advancements address challenges such as high-dimensional data, evolving attack vectors, and adaptive adversaries, aiming to strengthen the overall security, resilience, and trustworthiness of cloud computing platforms.