International Journal on Science and Technology

E-ISSN: 2229-7677     Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.

AI-Infused IAM: Revolutionizing Security for Operational Technology in Manufacturing

Author(s) Sudheer Kotilingala
Country United States
Abstract Identity and Access Management (IAM) has become vital for operational technology security in manufacturing environments as they embrace Industry 4.0 and face increased cyber threats. This article explores how AI-infused IAM solutions transform manufacturing security by addressing unique challenges in legacy system integration, scalability, and continuous protection. Advanced technologies including real-time threat detection, intelligent access control, behavioral authentication, and predictive risk management collectively enhance security posture while maintaining operational efficiency. The integration of artificial intelligence with traditional security frameworks enables manufacturing organizations to protect critical infrastructure against sophisticated attacks while simultaneously improving regulatory compliance and operator experience. Implementation considerations address the complexity of industrial environments, highlighting the necessary balance between automated security and human oversight to prevent operational disruptions while maintaining robust protection against evolving threats.
Keywords Artificial Intelligence, Cybersecurity, Industry 4.0, Manufacturing Technology, Operational Technology
Field Computer
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-08
Cite This AI-Infused IAM: Revolutionizing Security for Operational Technology in Manufacturing - Sudheer Kotilingala - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3344
DOI https://doi.org/10.71097/IJSAT.v16.i2.3344
Short DOI https://doi.org/g9fcgw

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