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Machine Learning–Enabled Governance Architecture for Smart Infrastructure Cybersecurity and Autonomous Enterprise Operations

Mélissa Saraiva

Abstract

The rapid evolution of smart infrastructure and autonomous enterprise systems has transformed the operational landscape of modern organizations. With the increasing integration of cloud computing, Internet of Things (IoT), artificial intelligence, and distributed digital platforms, enterprises face complex challenges related to cybersecurity, governance, and operational management. Traditional governance frameworks are often inadequate for managing dynamic and intelligent infrastructures where systems operate autonomously and continuously generate large volumes of operational data. This research proposes a Machine Learning–enabled governance architecture designed to enhance cybersecurity management, regulatory compliance, and autonomous decision-making within enterprise infrastructures.

 The proposed architecture integrates machine learning algorithms, data analytics, and automated governance mechanisms to monitor infrastructure behavior, detect cyber threats, enforce compliance policies, and support intelligent operational decisions. The framework introduces adaptive monitoring systems capable of identifying anomalies, predicting potential security vulnerabilities, and enabling proactive response mechanisms. By incorporating machine learning models within governance layers, enterprises can establish real-time risk assessment and automated policy enforcement across distributed infrastructures.

 This study analyzes existing governance models and identifies limitations in traditional cybersecurity management approaches. The research then develops a comprehensive governance architecture that integrates machine learning with enterprise operational systems. The findings suggest that machine learning–enabled governance significantly improves threat detection accuracy, operational transparency, and infrastructure resilience, thereby supporting secure and autonomous enterprise operations in modern digital ecosystems.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/vf1ysj05

Pages

12816-12826

Published

September 23, 2024
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mélissa Saraiva (%2024). Machine Learning–Enabled Governance Architecture for Smart Infrastructure Cybersecurity and Autonomous Enterprise Operations. International Journal of Science, Research and Technology , Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12816-12826. https://doi.org/10.15662/vf1ysj05

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