Intersection of AI and Cybersecurity: A Data-Driven Approach to Proactive Risk Management in ETL Processes - Shiva Kumar Vuppala (International Journal of Intelligent Systems and Applications in Engineering, 2025)
The growing complexity of Extract, Transform, Load (ETL) processes and their crucial role in modern data pipelines make them susceptible to various cybersecurity risks, including unauthorized access, data tampering, and service disruption. These threats can have far-reaching consequences, affecting business operations, regulatory compliance, and strategic decision-making. Traditional security approaches, relying on static rule-based systems, struggle to address the dynamic nature and scale of ETL workflows, necessitating the integration of more adaptive and intelligent methods. A data-driven approach utilizing Artificial Intelligence (AI) offers a promising solution by leveraging machine learning and deep learning techniques to continuously analyze system logs, performance metrics, and historical incidents for abnormal activity. This paper proposes a hybrid approach combining autoencoders for feature extraction and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) models for anomaly detection, aiming to proactively identify security risks within ETL systems. Autoencoders are employed to reduce data dimensionality while capturing critical features, while the CNN-GRU model enhances the detection of both local and temporal anomalies. The proposed method is evaluated through performance metrics, showing a high detection rate and minimal false positives compared to traditional rule-based methods. The results demonstrate the potential of AI-driven security frameworks to provide real-time, intelligent monitoring and adaptive risk management, thus improving ETL pipeline resilience and security. This research highlights the importance of incorporating AI into cybersecurity strategies for dynamic, data-intensive environments, ensuring that security measures evolve alongside emerging threats.