  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.
