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SAIS 2020 Proceedings

Title

The term “digital infrastructures” is used to refer to one or more of a combination of IoT and its artifacts, the cloud, cyber-physical systems, and digitized business architectures. As digital infrastructures become increasingly complex and interdependent, impacts from disruptive events have the potential to be more harmful than mere inconveniences and financial losses. Macintosh Troubleshooting Pocket Guide for Mac OS. Belgian Shepherd. Black giant squirrel with a nut, aka Javan Squirrel. Black swan, detail of head.

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Abstract

Black Swans Of Venus Mac OS

The term “digital infrastructures” is used to refer to one or more of a combination of IoT and its artifacts, the cloud, cyber-physical systems, and digitized business architectures. As digital infrastructures become increasingly complex and interdependent, impacts from disruptive events have the potential to be more harmful than mere inconveniences and financial losses. The risk from these catastrophic events to digital infrastructures may leave many organizations unprepared. To predict so-called “Black Swan Events' to increasingly complex digital infrastructures this research in progress postulates that risk management activities should be conducted outside of existing frameworks. In this paper, we argue that qualitative scenario risk modeling exercises utilizing diverse stakeholders may become even more important than other types of risk analysis in the prediction of threats to digital infrastructures. We discuss the importance of diverse stakeholders in developing structured, qualitative, scenario models to predict Black Swan Events to digital infrastructures. We discuss potential issues and solutions for the cataloging and quantification of the use cases developed from qualitative event scenario modeling and the next steps for this research.

Recommended Citation

Avery, Atiya, 'Cybersecurity Scenario Modeling: Imagining the Black Swans for Digital Infrastructures Risk Management' (2020). SAIS 2020 Proceedings. 5.
https://aisel.aisnet.org/sais2020/5

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