Design choices for productive, secure, data-intensive research at scale in the cloud

Diego Arenas; Jon Atkins; Claire Austin; David Beavan; Alvaro Cabrejas Egea; Steven Carlysle-Davies; Ian Carter; Rob Clarke; James Cunningham; Tom Doel; Oliver Forrest; Evelina Gabasova; James Geddes; James Hetherington; Radka Jersakova; Franz Kiraly; Catherine Lawrence; Jules Manser; Martin T. O'Reilly; James Robinson; Helen Sherwood-Taylor; Serena Tierney; Catalina A. Vallejos; Sebastian Vollmer; Kirstie Whitaker

doi:10.48550/arXiv.1908.08737

Design choices for productive, secure, data-intensive research at scale in the cloud

Diego Arenas, Jon Atkins, Claire Austin, David Beavan, Alvaro Cabrejas Egea, Steven Carlysle-Davies, Ian Carter, Rob Clarke, James Cunningham, Tom Doel, Oliver Forrest, Evelina Gabasova, James Geddes, James Hetherington, Radka Jersakova, Franz Kiraly, Catherine Lawrence, Jules Manser, Martin T. O'Reilly, James RobinsonHelen Sherwood-Taylor, Serena Tierney, Catalina A. Vallejos, Sebastian Vollmer, Kirstie Whitaker

Division of Informatics, Imaging & Data Sciences (L5)

Research output: Preprint/Working paper › Preprint

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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.

Original language	English
Publisher	arXiv
Pages	1-23
Number of pages	23
DOIs	https://doi.org/10.48550/arXiv.1908.08737
Publication status	Published - 23 Aug 2019

Keywords

cs.CR

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10.48550/arXiv.1908.08737

1908.08737v2

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Arenas, D., Atkins, J., Austin, C., Beavan, D., Egea, A. C., Carlysle-Davies, S., Carter, I., Clarke, R., Cunningham, J., Doel, T., Forrest, O., Gabasova, E., Geddes, J., Hetherington, J., Jersakova, R., Kiraly, F., Lawrence, C., Manser, J., O'Reilly, M. T., ... Whitaker, K. (2019). Design choices for productive, secure, data-intensive research at scale in the cloud. (pp. 1-23). arXiv. https://doi.org/10.48550/arXiv.1908.08737

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title = "Design choices for productive, secure, data-intensive research at scale in the cloud",

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. ",

keywords = "cs.CR",

author = "Diego Arenas and Jon Atkins and Claire Austin and David Beavan and Egea, {Alvaro Cabrejas} and Steven Carlysle-Davies and Ian Carter and Rob Clarke and James Cunningham and Tom Doel and Oliver Forrest and Evelina Gabasova and James Geddes and James Hetherington and Radka Jersakova and Franz Kiraly and Catherine Lawrence and Jules Manser and O'Reilly, {Martin T.} and James Robinson and Helen Sherwood-Taylor and Serena Tierney and Vallejos, {Catalina A.} and Sebastian Vollmer and Kirstie Whitaker",

year = "2019",

month = aug,

day = "23",

doi = "10.48550/arXiv.1908.08737",

language = "English",

pages = "1--23",

publisher = "arXiv",

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type = "WorkingPaper",

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Arenas, D, Atkins, J, Austin, C, Beavan, D, Egea, AC, Carlysle-Davies, S, Carter, I, Clarke, R, Cunningham, J, Doel, T, Forrest, O, Gabasova, E, Geddes, J, Hetherington, J, Jersakova, R, Kiraly, F, Lawrence, C, Manser, J, O'Reilly, MT, Robinson, J, Sherwood-Taylor, H, Tierney, S, Vallejos, CA, Vollmer, S & Whitaker, K 2019 'Design choices for productive, secure, data-intensive research at scale in the cloud' arXiv, pp. 1-23. https://doi.org/10.48550/arXiv.1908.08737

TY - UNPB

T1 - Design choices for productive, secure, data-intensive research at scale in the cloud

AU - Arenas, Diego

AU - Atkins, Jon

AU - Austin, Claire

AU - Beavan, David

AU - Egea, Alvaro Cabrejas

AU - Carlysle-Davies, Steven

AU - Carter, Ian

AU - Clarke, Rob

AU - Cunningham, James

AU - Doel, Tom

AU - Forrest, Oliver

AU - Gabasova, Evelina

AU - Geddes, James

AU - Hetherington, James

AU - Jersakova, Radka

AU - Kiraly, Franz

AU - Lawrence, Catherine

AU - Manser, Jules

AU - O'Reilly, Martin T.

AU - Robinson, James

AU - Sherwood-Taylor, Helen

AU - Tierney, Serena

AU - Vallejos, Catalina A.

AU - Vollmer, Sebastian

AU - Whitaker, Kirstie

PY - 2019/8/23

Y1 - 2019/8/23

N2 - 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.

AB - 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.

KW - cs.CR

U2 - 10.48550/arXiv.1908.08737

DO - 10.48550/arXiv.1908.08737

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EP - 23

BT - Design choices for productive, secure, data-intensive research at scale in the cloud

PB - arXiv

ER -

Design choices for productive, secure, data-intensive research at scale in the cloud

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