Proactive Security for the Industrial IoT Networks in the AI Era: A Framework for Continuous Threat Exposure Management

This article investigates the convergence of Industrial Internet of Things (IIoT) technologies and Artificial Intelligence (AI), exploring the evolving landscape of AI-powered attacks and the critical need for proactive security measures. To mitigate these risks, this paper proposes a multi-faceted security approach that combines traditional security techniques with AI-driven solutions. Focusing on Continuous Threat Exposure Management (CTEM) and Attack Surface Management (ASM), and emphasizing real-time threat detection, automated response mechanisms, and a robust human-AI collaboration model, the study explores a paradigm shift from reactive to proactive security measures in response to the evolving cyber threats facing modern industrial environments. Through an in-depth analysis of successful security implementations across diverse industrial sectors, this research demonstrates the efficacy of integrating AI-driven security measures with existing infrastructure. By proactively addressing these challenges, organizations can harness the power of AI to safeguard their digital assets and ensure the integrity of their operations.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-02-03
DOI
https://doi.org/10.32628/cseit251112162
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Proactive Security for the Industrial IoT Networks in the AI Era: A Framework for Continuous Threat Exposure Management

Amith Ronad
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Adversarial Robustness in Machine Learning
article

Proactive Security for the Industrial IoT Networks in the AI Era: A Framework for Continuous Threat Exposure Management

Amith Ronad
article en

Abstract

This article investigates the convergence of Industrial Internet of Things (IIoT) technologies and Artificial Intelligence (AI), exploring the evolving landscape of AI-powered attacks and the critical need for proactive security measures. To mitigate these risks, this paper proposes a multi-faceted security approach that combines traditional security techniques with AI-driven solutions. Focusing on Continuous Threat Exposure Management (CTEM) and Attack Surface Management (ASM), and emphasizing real-time threat detection, automated response mechanisms, and a robust human-AI collaboration model, the study explores a paradigm shift from reactive to proactive security measures in response to the evolving cyber threats facing modern industrial environments. Through an in-depth analysis of successful security implementations across diverse industrial sectors, this research demonstrates the efficacy of integrating AI-driven security measures with existing infrastructure. By proactively addressing these challenges, organizations can harness the power of AI to safeguard their digital assets and ensure the integrity of their operations.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(1)
U.S. Global Leadership Coalition (US)
Openalex Percentile: Top 99%
Adversarial Robustness in Machine Learning
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