With the rapid development of cloud computing and virtualization technology, information security issues have become increasingly prominent. Especially in virtualized environments, the spread of attack behaviors and malware has continued to evolve, posing a huge challenge to traditional security protection mechanisms. In order to address this issue, this paper studies the application of innovative algorithms that integrate virtualization and artificial intelligence in cloud services based on information security needs. This paper proposes a security protection solution that combines autoencoders and clustering algorithms. The autoencoder is used to automatically identify normal and abnormal traffic in a virtualized environment, and the clustering algorithm is used to classify different types of attacks, thereby improving the system’s protection capabilities. The algorithm can dynamically adjust firewall rules, automatically detect and respond to high-risk IPs and abnormal traffic, and restrict access to abnormal ports. Experimental results show that the security protection mechanism based on this algorithm performs well in detection accuracy, attack mitigation rate, and real-time response capability. For normal traffic detection, the classification accuracy is 90% and the false alarm rate is 14.30%. This means that the system is more accurate in identifying normal traffic and has achieved a high success rate in attack mitigation. These results show that the innovative algorithm that integrates virtualization and artificial intelligence can effectively improve the security protection capabilities in cloud service environments and ensure the security of data and resources.