Integrating Blockchain and AI for Data Encryption and Secure ETL Pipelines
Keywords:
Blockchain, ETL Pipeline, MLP-GRU, Anomaly Detection, Data Security, Compliane, Artificial Intelligence.Abstract
In the era of data-driven decision-making, ensuring the security, transparency, and integrity of Extract, Transform, and Load (ETL) pipelines has become increasingly critical, especially in regulated industries such as healthcare, finance, and telecommunications. Traditional ETL systems often rely on centralized architectures with basic encryption and access control mechanisms, which, although essential, fall short of addressing sophisticated cyber threats, data tampering, and compliance verification. This research proposes a hybrid framework that integrates Blockchain technology and an MLP-GRU (Multi-Layer Perceptron – Gated Recurrent Unit) neural network to enhance the security and intelligence of ETL processes. Blockchain is employed to create a decentralized, tamper-proof ledger that logs each ETL operation, providing traceability, immutability, and auditability. In parallel, the MLP-GRU model is utilized to detect anomalies in ETL activities by analyzing both static and sequential log data. This dual approach ensures not only secure data management but also real-time monitoring and predictive threat mitigation. The experimental setup involves blockchain-based logging of ETL operations and AI-based anomaly detection, evaluated using metrics such as Accuracy (99%), Precision (98.21%), Recall (98%), and F1-Score (98.77%). Results demonstrate that the integrated system outperforms traditional ETL security mechanisms in detecting malicious activity while maintaining efficient data throughput and low latency. Furthermore, the study examines blockchain transaction performance under varying data volumes to validate the scalability of the proposed solution. The framework's ability to automate compliance verification and generate immutable audit trails presents a significant advancement in secure data pipeline design. Future work includes enhancing privacy through Zero-Knowledge Proofs, scaling to federated systems, and incorporating advanced deep-learning architectures. Overall, this research sets a strong foundation for the development of intelligent, secure, and regulation-compliant ETL infrastructures through the convergence of blockchain and AI technologies.
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