Authors : Aakarsh Mavi

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/mry99k76

Scribd : https://tinyurl.com/fm6nb4ct

DOI : https://doi.org/10.38124/ijisrt/25jul1859

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Abstract : The convergence of Information Technology (IT) and Operational Technology (OT) in HVAC manufacturing is unlocking new levels of operational efficiency, automation, and data-driven decision-making. However, this digital unification also introduces significant cybersecurity challenges. Historically isolated, IT and OT systems were built with distinct goals and security models, making their integration a prime target for vulnerabilities and exploitation. This research explores a proactive approach to securing smart HVAC manufacturing by automating security audits across these blended environments. The core objective is to design intelligent systems that continuously and autonomously assess the security posture of both IT and OT domains, especially at their intersection where traditional security frameworks often fall short. By automating vulnerability identification and assessment, the proposed framework aims to bridge critical gaps in visibility, communication, and threat response between IT and OT layers. The methodology emphasizes real-time monitoring, AI-driven anomaly detection, and automated patch management to ensure swift remediation of emerging threats. Leveraging advanced security automation tools including machine learning- based analytics, this approach reduces reliance on manual processes, minimizes human error, and provides scalable, adaptive defense mechanisms. Ultimately, the research contributes to building resilient, future-ready HVAC manufacturing systems that not only optimize performance through IT/OT unity but also stand strong against an evolving cyber threat landscape.

Keywords : Metadata Management, AI-Based File Management, Machine Learning, Natural Language Processing (NLP), Cloud Integration, Document Security, Role-Based Access Control (RBAC), Anomaly Detection, Encryption, Duplicate Detection, Intelligent Search, Regulatory Compliance, IoT Integration, Predictive Analytics, Blockchain for Document Authentication, Automated Workflow, Secure Data Storage, Audit Logs, Smart File Categorization, DNS.

References :

1. CISA. Cross-Sector Cybersecurity Performance Goals (CPGs). https://www.cisa.gov/cpg. 2022.
2. International Electrotechnical Commission. IEC 624432-1: Security for Industrial Automation and Control Systems. IEC, 2010.
3. Osha Shukla. “Enhancing Threat Intelligence and Detection with Real-Time Data Integration”. In: International Journal of Engineering Research Technology (IJERT) 14.04 (2024), pp. 91–96. URL: https://www.ijert.org/ research/enhancing-threat-intelligence-and-detectionwith-real-time-data-integration-IJERTV14IS040201.pdf.
4. Osha Shukla. “Software Supply Chain Security: Designing a Secure Solution with SBOM for Modern Software Ecosystems”. In: International Journal of Engineering Research Technology (IJERT) 13.02 (2024), pp. 1–7.
5. URL: https://www.ijert.org/software- supply- chainsecurity-designing-a-secure-solution-with-sbom-formodern-software-ecosystems.
6. Robin Sommer and Vern Paxson. “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection”. In: IEEE Symposium on Security and Privacy. 2010.
7. National Institute of Standards and Technology (NIST). Framework for Improving Critical Infrastructure Cybersecurity. Tech. rep. U.S. Department of Commerce, 2018. URL: https://nvlpubs.nist.gov/nistpubs/CSWP/NIST. CSWP.04162018.pdf.