AI-augmented blockchain for cloud data integrity assurance: Building trustworthy, tamper-proof data systems in distributed clouds

The integrity of data stored in cloud environments is a critical concern as organizations increasingly rely on distributed cloud services for their data storage and processing needs. Traditional methods of data integrity assurance, such as cryptographic hashing and third-party audits, often fail to provide the required levels of security due to their centralized nature and reliance on trust in third-party entities. This paper proposes an AI augmented blockchain framework designed to provide a robust, decentralized solution for data integrity assurance in distributed cloud environments. By integrating Artificial Intelligence (AI) with blockchain technology, the proposed framework offers real-time anomaly detection, improved decision-making, and enhanced scalability for cloud data systems.
The architecture utilizes a lightweight consensus mechanism, such as Delegated Proof of Stake (DPoS), to reduce latency and computational costs, while AI models are employed to identify anomalies in data transactions before they are permanently recorded on the blockchain. Experimental results show that the proposed system achieves a data integrity assurance rate of 98.5%, significantly outperforming traditional blockchain methods. Moreover, the framework reduces average transaction latency by 35% and increases transaction throughput by 43%. This results in a more efficient and sustainable approach to data integrity management, making it suitable for large-scale cloud deployments. The findings suggest that the AI-augmented blockchain framework can serve as a foundational solution for building trustworthy, tamper-proof data systems, with applications ranging from enterprise cloud services to secure Internet of Things (IoT) deployments. These advancements contribute to a more secure and resilient cloud computing environment, addressing the evolving challenges of data integrity and security.