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Readiness Exercises: Are Risk Assessment Methodologies Ready for the Cloud?

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Advances in Core Computer Science-Based Technologies

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 14))

Abstract

Cloud computing is a type of service that allows the use of computing resources from a distance, rather than a new technology. Various services exist on-demand, ranging from data storage and processing to software as a service, like email and developing platforms. Cloud computing enables ubiquitous, on-demand access over the net to a shared pool of configurable resources, like servers, applications, etc. that can be accessed, altered or even restored rapidly with minimal service provider interaction or management effort. Still, due to the vast growth of cloud computing, new security issues have been introduced. Key factors are the loss of control over any outsourced resources and cloud’s computing inherent security vulnerabilities. Managing these risks requires the adoption of an effective risk management method, capable of involving both the Cloud customer and the Cloud Service Provider. Risk assessment methods are common tools amongst IT security consultants for managing the risk of entire companies. Still, traditional risk management methodologies are having trouble managing cloud services. Extending our previous work, the purpose of this paper is to compare and examine whether popular risk management methods and tools (e.g. NIST SP800, EBIOS, MEHARI, OCTAVE, IT-Grundschutz, MAGERIT, CRAMM, HTRA, Risk-Safe Assessment, CORAS) are suitable for cloud computing environments. Specifically, based upon existing literature, this paper points out the essential characteristics that any risk assessment method addressed to cloud computing should incorporate, and suggests three new ones that are more appropriate based on their features.

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Gritzalis, D., Stergiopoulos, G., Vasilellis, E., Anagnostopoulou, A. (2021). Readiness Exercises: Are Risk Assessment Methodologies Ready for the Cloud?. In: Tsihrintzis, G., Virvou, M. (eds) Advances in Core Computer Science-Based Technologies. Learning and Analytics in Intelligent Systems, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-41196-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-41196-1_6

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