Trim, Peter R.J. and Lee, Y.-I. (2022) Combining sociocultural intelligence with Artificial Intelligence to increase organizational cyber security provision through enhanced resilience. Big Data and Cognitive Computing 6 (4), p. 110. ISSN 2504-2289.
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Abstract
Although artificial intelligence (AI) and machine learning (ML) can be deployed to improve cyber security management, not all managers understand the different types of AI/ML and how they are to be deployed alongside the benefits associated with sociocultural intelligence. The aim of this paper was to provide a context within which managers can better appreciate the role that sociocultural intelligence plays so that they can better utilize AI/ML to facilitate cyber threat intelligence (CTI). We focused our attention on explaining how different approaches to intelligence (i.e., the intelligence cycle (IC) and the critical thinking process (CTP)) can be combined and linked with cyber threat intelligence (CTI) so that AI/ML is used effectively. A small group interview was undertaken with five senior security managers based in a range of companies, all of whom had extensive security knowledge and industry experience. The findings suggest that organizational learning, transformational leadership, organizational restructuring, crisis management, and corporate intelligence are fundamental components of threat intelligence and provide a basis upon which a cyber threat intelligence cycle process (CTICP) can be developed to aid the resilience building process. The benefit of this is to increase organizational resilience by more firmly integrating the intelligence activities of the business so that a proactive approach to cyber security management is achieved.
Metadata
Item Type: | Article |
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Keyword(s) / Subject(s): | artificial intelligence, cyber security manager, cyber threat intelligence, learning, resilience, sociocultural intelligence |
School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
Depositing User: | Administrator |
Date Deposited: | 18 Oct 2022 11:08 |
Last Modified: | 02 Aug 2023 18:18 |
URI: | https://eprints.bbk.ac.uk/id/eprint/49449 |
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