A Privacy-Aware Framework for Distributed AI in Regulated Environments

Abstract

With the extensive growth in terms of data and AI adoption across various fields such as finance, healthcare and insurance, data security and privacy have become significant barriers to innovation. This paper provides a privacy-aware framework for distributed AI as a possible solution which is integrated with cloud-native architectures. By leveraging decentralized model training without sharing raw data, this solution offers a compliant and secure framework for deploying machine learning at scale. A scalable and cost-effective system architecture is proposed that aligns with data protection regulations while maintaining high performance and model accuracy. This approach empowers organizations to leverage AI responsibly, unlocking the potential of sensitive data without compromising privacy.