AI-Driven Integration of Machine Learning in Smart Connect Ecosystems: Distributed Sustainable IT Modernization and NLP-Governed Data Policy Frameworks
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Abstract
The rapid evolution of smart connect ecosystems—spanning smart cities, IoT devices, autonomous systems, and digital infrastructure—demands robust mechanisms to ensure security, sustainability, and responsible data governance. This paper explores the integration of Deep Neural Networks (DNNs) and Natural Language Processing (NLP) to enhance the intelligence, adaptability, and trustworthiness of such ecosystems. We propose a data governance-driven framework that leverages the predictive power of DNNs and the semantic capabilities of NLP to automate anomaly detection, policy enforcement, and context-aware decision-making across heterogeneous networks. The study emphasizes how AI-powered data processing and language understanding can identify security breaches, streamline compliance with data protection regulations, and support sustainable data lifecycle management. Through experimental evaluation and real-world case studies, we demonstrate the efficacy of our approach in fostering a secure and resilient smart connect infrastructure. The paper concludes by outlining future research directions, including ethical AI deployment, explainability, and cross-domain interoperability in smart systems.
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