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In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic\/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI\/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI\/ML technologies are migrated to the periphery of IoT networks.<\/jats:p>","DOI":"10.1186\/s42400-020-00052-8","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T00:02:13Z","timestamp":1591056133000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains"],"prefix":"10.1186","volume":"3","author":[{"given":"Petar","family":"Radanliev","sequence":"first","affiliation":[]},{"given":"David","family":"De Roure","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Page","sequence":"additional","affiliation":[]},{"given":"Jason R. 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