Preventing Data Inconsistency in Distributed Architectures Using AI-Driven Synchronization and Governance Models

Vikrant Bhateja

doi:10.15662/IJRAI.2021.0404005

Authors

Vikrant Bhateja Veer Bahadur Singh Purvanchal University, Jaunpur Uttar Pradesh, India Author

DOI:

https://doi.org/10.15662/IJRAI.2021.0404005

Keywords:

Distributed systems, data consistency, artificial intelligence, synchronization, data governance, machine learning, conflict resolution, eventual consistency, distributed databases, anomaly detection

Abstract

Distributed architectures have become the backbone of modern digital systems, enabling scalability, resilience, and global accessibility. However, maintaining data consistency across distributed nodes remains a critical challenge due to latency, partial failures, and concurrent updates. Traditional consistency models, such as eventual consistency and strong consistency, often involve trade-offs between performance and reliability. This research explores the role of artificial intelligence (AI) in enhancing synchronization and governance mechanisms to mitigate data inconsistency. AI-driven synchronization models leverage machine learning algorithms to predict conflicts, optimize replication strategies, and dynamically adjust consistency levels based on workload patterns. Additionally, governance frameworks powered by AI can enforce data policies, monitor anomalies, and ensure compliance across distributed environments. The study proposes an integrated model combining predictive analytics, intelligent conflict resolution, and adaptive governance to maintain data integrity. Through conceptual analysis and methodological design, this research highlights how AI can significantly reduce inconsistency risks while preserving system performance. The findings suggest that AI-driven approaches offer a promising direction for next-generation distributed systems, enabling more autonomous, efficient, and reliable data management across complex infrastructures.

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