Design Choices for Productive, Secure, Data-Intensive Research at Scale in the Cloud

Resource type
Journal article
Creator (person)
Arenas, Diego
Atkins, Jon
Austin, Claire
Beavan, David
Cabrejas Egea, Alvaro
Carlysle-Davies, Steven
Carter, Ian
Clark, Rob
Cunningham, James
Doel, Tom
Forrest, Oliver
Gabasova, Evelina
Geddes, James
Hetherington, James
Jersakova, Radka
Kiraly, Franz
Lawrence, Catherine
Manser, Jules
O'Reilly, Martin T.
Robinson, James
Sherwood-Taylor, Helen
Tierney, Serena
Vallejos, Catalina A.
Vollmer, Sebastian
Whitaker, Kirstie
Date published
2019
Abstract
We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user device control, and analysis environments. By presenting design patterns for security choices for each tier, and using software defined infrastructure so that a different, independent, secure research environment can be instantiated for each project appropriate to its classification, we hope to maximise researcher productivity and minimise risk, allowing research organisations to operate with confidence.
Contributor (organisation)
British Library
Living with Machines
Project(s)
Living with Machines
Funder
Funder nameAwards
Arts and Humanities Research Council
AH/S01179X/1
Journal title
arXiv
Publisher
arXiv
Official URL
Collection