Integrating Artificial Intelligence With Blockchain For Secure And Scalable Data Management In Decentralized Systems

Abstract

The rapid proliferation of the decentralized systems has instigated the need of secure, scalable, and efficient systems of information management. Blockchain is not only immutable but also vulnerable and trusted and performs poorly in the aspects of scalability and high latencies and computation overheads. On the quest to find them, AI (Artificial Intelligence) can provide predictive mechanisms, optimization and anomaly detection, but to carry out its operations efficiently, it requires reputable datasets. This research paper discusses the way AI can be integrated with blockchain so as to expand decentralized data ecosystems. It applied four algorithms Federation Learning as a data training distribution method, reinforcing learning as a consensus optimization method, deep learning in detecting anomalies, and genetic algorithm resource optimization method. They showed a substantial improvement in performance: the throughput has improved by 37 per cent, the latency has reduced by 42 per cent, and the energy consumption has dropped by 28 per cent as compared to the initial blockchain based systems. In addition, the precision of the anomaly detection amounted to 94.6, which strengthens the credibility of decentralized data. It has been established by the comparative analysis with the corresponding works that the developed integration has demonstrated the greatest presence of scalability and resilience with the high standards of security. It is the conclusion of this paper to conclude that AI-Blockchain convergence is a ground-breaking location of secure intelligent and scalable data management. It is directly related to the cyber world through matters of industries such as healthcare, finance, internet of things and supply chain within which handling sensitive data and efficiency are mattered.