Intelligent Data Engineering and AI Integration for Modern Secure and Scalable Enterprise Systems
DOI:
https://doi.org/10.15662/IJRAI.2026.0903001Keywords:
Intelligent Data Engineering, Artificial Intelligence, Enterprise Systems, Data Pipelines, Scalability, Data Security, Machine Learning, Data Governance, Cloud Computing, Big Data Analytics, Automation, CybersecurityAbstract
The rapid evolution of enterprise systems has necessitated the integration of intelligent data engineering practices with advanced artificial intelligence (AI) technologies to ensure scalability, security, and efficiency. Modern enterprises generate vast volumes of structured and unstructured data, requiring robust architectures capable of real-time processing, adaptive learning, and secure handling of sensitive information. Intelligent data engineering encompasses automated data pipelines, data quality management, and scalable storage solutions, while AI integration enables predictive analytics, anomaly detection, and decision automation.
This study explores the synergy between data engineering and AI in building secure and scalable enterprise systems. It highlights architectural patterns such as data lakes, data meshes, and hybrid cloud infrastructures, alongside AI-driven optimization techniques. Security considerations, including data privacy, encryption, and compliance frameworks, are also examined in the context of AI-enabled systems. Furthermore, the paper evaluates challenges such as data governance, model bias, and infrastructure complexity.
The research concludes that the integration of intelligent data engineering with AI significantly enhances operational efficiency, supports real-time decision-making, and strengthens system resilience. However, careful design, governance, and ethical considerations are critical to ensuring long-term sustainability and trustworthiness in enterprise environments.
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