Data Safe Haven Classification and Trusted environments in the cloud: extending a Turing Django based application across multiple institutions

Rebecca Osselton, Newcastle University

Data Safe Havens provide a cloud deployed secure and robust research environment for dataset exploration. This data may be sensitive in nature and the Data Safe Haven gives institutions a trusted environment in which to develop and extend their research. Safe Havens require a level of security classification from where no sensitive data is used, to the highest level of security, such as those needed by governments and defence agencies. The Data Safe Haven Classification app is a web-based Information Governance application that guides stakeholders through a process to determine the correct level of classification. Users have defined roles within the system and must answer a sequence of questions to determine the correct level of security. The app exists independently from the Safe Haven environment and allows the linking of multiple datasets across work packages, giving flexibility to institutions, while holding no sensitive data internally. Work to improve and increase the portability of the classification app is underway with multiple institutions including University College London, Newcastle University, University of Cambridge and the Alan Turing Institute. This presentation will discuss features of the app and challenges in its distribution across different institutions, in terms of technology and policy.

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