AI-Driven Operational Intelligence and Lineage Automation in Distributed Federal Cloud Systems - Aditya Swaprakash Gadepalli Sri Pratyak (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Federal cloud systems have seen the introduction of Artificial Intelligence (AI) more than ever before — but pre-2022, that meant that the monitoring of these systems and their data lineage tools were manual and disconnected. This is a secondary study reviewing literature published in Google Scholar, IEEE Xplore, ScienceDirect, Springer and government reports that investigates operational intelligence and lineage automation powered by AI in a Distributed Federal Cloud System. The key takeaways highlight faster anomaly detection, real-time analytics capabilities, and the decrease in manual effort required, as well as lineage automation for boosted data traceability, higher audit preparedness, and better compliance with regulatory requirements such as FedRAMP and NIST standards. These technologies combined provided security, governance and operational benefits. Yet, there was always a hurdle to jump like integration complexity, lack of data silos, lack of skills, and algorithmic bias issues. Overall, AI and intelligent automation have proved to be valuable for advancing federal cloud operations before 2022, but fragmented governance and interoperability challenges held back their full value. Standardization and transparency will be essential to future progress of standardized policies and explainable AI. DOI: https://doi.org/10.17762/ijisae.v10i2s.8383