Title: Efficiency of the framework for industrial information security management utilising machine learning techniques

Authors: Nisha Nandal; Naveen Negi; Aarushi Kataria; Rita Shokeen

Addresses: Department of Management, Bharati Vidyapeeth (Deemed to be University) Institute of Management and Research, Bharati Vidyapeeth University, New Delhi, India ' School of Management, Graphic Era Hill University, Dehradun, Uttarakhand, India ' Department of Finance and Marketing, Bharati Vidyapeeth Institute of Management and Research, New Delhi, India ' Department of Management, Jagan Institute of Management Studies, Rohini, New Delhi, India

Abstract: Discover the innovative integration of crowd sense technology and artificial intelligence in the industrial machine learning (ML) mining sphere. This fusion transcends data processing to encompass meticulous safety monitoring via collective knowledge management. Envision a harmonised framework where management of keys, tables, hardware, and ML mining supervision coalesce to shield enterprise data robustly. This approach, examined through various lenses, including security and big data capacity testing, assesses risk mitigation enthusiastically while crafting a business management platform that contemplates corporate leadership needs, offering an ML data security architecture blueprint. Although challenges like refining neural networks for optimal global efficiency persist, the study highlights its remarkable, unblemished performance across modules on the ML-based corporate data safety regulation platform. It proficiently meets daily organisational needs and assures AI's vital role in enterprise data security management, providing a scaffold for future research and marking a paradigm for upcoming explorations in the domain.

Keywords: artificial intelligence; AI; industrial information; security management; machine learning techniques; crowd sense technology; information security management.

DOI: 10.1504/IJCIS.2026.153812

International Journal of Critical Infrastructures, 2026 Vol.22 No.2, pp.133 - 156

Received: 09 Aug 2023
Accepted: 11 Dec 2023
Published online: 27 May 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article