Abstract

The increasing use of artificial intelligence (AI) applications in cloud and edge environments raises significant concerns regarding security, data integrity, and the trustworthiness of models. Traditional security measures often struggle to provide adequate protection against tampering, unauthorized access, and privacy breaches. However, blockchain technology—with its decentralized architecture, immutability, and transparency—offers a promising solution to these challenges. This research explores the integration of blockchain in the development of AI for cloud and edge environments, emphasizing its potential to secure data provenance, protect AI models, and enable privacy-preserving learning. By analyzing use cases such as federated learning, decentralized AI marketplaces, and edge device security, this study provides insights into the opportunities and challenges presented by this convergence. The proposed framework highlights scalable blockchain solutions that align with the performance requirements of modern AI systems, offering a pathway for secure and trustworthy AI development in distributed settings