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Barriers and opportunities in cyber risk and compliance management for data-driven supply chains

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
LDR  00000cam a22000004b 4500
001  MAP20220006385
003  MAP
005  20220911210946.0
008  220223s2022 usa|||| ||| ||eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎861
24510‎$a‎Barriers and opportunities in cyber risk and compliance management for data-driven supply chains‎$c‎Williams Afrifah... [et.al]
260  ‎$a‎Hawaii‎$b‎Hawaii International Conference on System Sciences‎$c‎2022
300  ‎$a‎10 p.
520  ‎$a‎In today's highly competitive market, where globalisation, mass production, and specialisation characterise the interconnected industrialised society, integrated Supply Chains (SCs) are more important than ever. Decision-makers rely on precise SC data, and even the smallest interruption in the data flow can have a substantial impact on the quality of the decisions. This dependency has inadvertently driven device connectivity towards an Industrial Internet of Things (IIoT) approach in the complete interconnection paradigm. While interconnectivity between devices has accelerated, IIoT and Industry 4.0 SC security measures have not kept pace. This conflict is exacerbated further by the need to process the data produced by IIoT systems while maintaining data privacy. With personal data being an integral part of the SC paradigm, this paper provides an extensive review of academic sources and consulting reports and presents a comprehensive analysis of the SC management barriers in Industry 4.0, as well as the role that advancements in Blockchain and artificial intelligence (AI) can play in mitigating SC security management risks.
650 4‎$0‎MAPA20160007633‎$a‎Ciberriesgos
650 4‎$0‎MAPA20140023066‎$a‎Ciberataques
650 4‎$0‎MAPA20140022700‎$a‎Ciberseguridad
650 4‎$0‎MAPA20080600082‎$a‎Cadena del suministro
650 4‎$0‎MAPA20080592875‎$a‎Protección de datos
650 4‎$0‎MAPA20150010735‎$a‎Internet de las cosas
650 4‎$0‎MAPA20080597580‎$a‎Medidas de seguridad
650  ‎$0‎MAPA20220007825‎$a‎Data driven
7001 ‎$0‎MAPA20220002028‎$a‎Afrifah, Williams