Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Divakaran, Prakash; Parnami, Tanmay; Gaur, Sonali; Baryah, Neha; Gupta, Komal; Upreti, Kamal Title A name given to the resource Strategic Integration of AI in Modern Data Management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/WorldSUAS66815.2025.11199110" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WorldSUAS66815.2025.11199110</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022276679?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022276679?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Divakaran P., Himalayan University, Department of Management, Arunchal Pradesh, India; Parnami T., GGSIPU, Dept. of Computer Science &amp; Engineering, New Delhi, India; Gaur S., EMPI Business School, Dept. of Management, New Delhi, India; Baryah N., Chandigarh Group of Colleges, Chandigarh School of Business, Department of Forensic Sciences, Punjab, Mohali, India; Gupta K., Finance Manager Accenture, Bangalore, India; Upreti K., Christ University, Department of Computer Science, Ghaziabad, India Description An account of the resource The exponential growth of data from sources such as social media, IoT, and enterprise systems has catalyzed a transformative shift in data management practices. This paper explores the integration of artificial intelligence (AI), edge computing, cloud-native frameworks, and graph-based techniques to support intelligent, low-latency, and scalable data processing across complex ecosystems. It presents a comparative analysis of classical versus modern data architectures, highlighting how technologies like Graph Neural Networks (GNN4TS), reinforcement learning, and large language models (LLMs) enable more adaptive, interpretable, and automated pipelines. The study also addresses challenges in legacy system modernization, time-series modeling, and cyber threat detection while underscoring the role of AI in autonomous database management and metadata enrichment. Further, it examines critical risks - including explainability, adversarial vulnerabilities, concept drift, and privacy preservation - associated with AI-integrated data workflows. A structured overview of emerging paradigms such as neuro-symbolic AI, adaptive governance in multi-agent systems, and the potential of quantum computing provides a future-focused lens on intelligent data ecosystems. The insights presented aim to assist researchers, data engineers, and decision-makers in navigating the evolving landscape of AI-driven data management. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Automation; Challenges; Data Management; Traditional Techniques Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153925-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online