Protecting Cloud Computing Environments from Cyber Threats with AI-Powered Machine Learning Systems

Cyberattacks on cloud computing have increased dramatically in recent years due to the technology's central role in today's digital infrastructure. The scattered and ever-changing nature of cloud systems makes it difficult for traditional security measures to identify and counteract sophisticated attackers. With an emphasis on their capacity to identify, avert, and react to cyber dangers in real-time, this study investigates the use of AI and ML technologies to improve cloud security. By analyzing massive databases for trends, predicting harmful actions, and mitigating assaults before they do harm, AI-powered solutions provide an adaptable, automated method to cloud security. Cloud security features including intrusion detection, anomaly detection, and threat intelligence are among those studied in this research, along with supervised, unsupervised, and reinforcement learning machine learning models. Artificial intelligence (AI)-powered systems may swiftly and accurately identify sophisticated multi-vector cyberattacks, zero-day attacks, insider threats, and other forms of cybercrime by examining network traffic, user behavior, and system vulnerabilities. Data privacy, scalability, and the possibility of adversarial assaults on AI models are some of the issues that this study resolves as they pertain to cloud AI integration. The practical benefits of cloud security solutions powered by AI are demonstrated through case studies from areas like e-commerce, healthcare, and finance. Improving the interpretability of AI models, making them more scalable, and making sure they comply with regulations so they may be used more widely are all topics covered in the paper's discussion of future research directions.