AI-Generated Privacy-Preserving Protocols for Cross-Cloud Data Sharing and Collaboration

The integration of artificial intelligence into cross-cloud data-sharing frameworks opens up completely new vistas for novelty in preserving privacy while at the same time increasing collaborative efficiencies. This project closely examines designing and implementing AI-generated protocols that protect sensitive data privacy exchanged between heterogeneous cloud environments. These would be the protocols using machine learning algorithms for runtime vulnerability and risk detection, dynamic flow encryption, and predefined privacy policies. This research would be based on leveraging federated learning and differential privacy techniques to ensure that the best way to optimize shared model accuracy is to follow all data protection regulation compliances. The empirical results indicate that the proposed protocol metrics outperform the state-of-the-art methods in maintaining data integrity, minimizing leakage risks, and enhancing data interoperability in a multi-cloud architecture. It contributes toward bettering secure collaboration processes across various verticals, including healthcare, finance, and telecommunications. The study further underlines the importance of AI-driven solution imperatives toward strengthening data privacy across distributed cloud systems.