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

arXiv: https://arxiv.org/abs/1908.08737 · HTML: https://mlsanity.com/paper/1908.08737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)](https://mlsanity.com/paper/2211.01656/) (2022)
- [Safe AI for health and beyond -- Monitoring to transform a health service](https://mlsanity.com/paper/2303.01513/) (2023)
- [How to Data in Datathons](https://mlsanity.com/paper/2309.09770/) (2023)

## Abstract (arXiv)

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.