Cloud Cybersecurity for Research Engineers

Abstract

The availability of large datasets and breakthroughs in machine learning have had a profound impact on how modern science is conducted. Once the preserve of computer science, large-scale compute has become a universal requirement across research fields from astrophysics to zoology. This has created a huge demand for computing infrastructure within research institutions, where academics often compete for scarce resources. Cloud computing offers a viable alternative. Cloud platforms such as Azure, Google Cloud and AWS offer researchers abundant pay-as-you-go computing resources at competitive prices, without the need to buy hardware or manage software. This flexibility is welcome, but comes with the caveat that operating in the public cloud means working on the open internet without professional IT support. Without appropriate measures, conducting scientific research on the internet exposes researchers and their institutions to security threats that, in the worst-case, could end in sensitive data leaks and ICO fines. In this walkthrough, we will showcase best practices for performing research on the public cloud securely, using a motivating example of patient data analysis performed on Azure. We will cover: Improving user authentication with 2FA and password protocols. Protecting your virtual network using a firewall. Using a remote desktop gateway for accessing virtual machines securely. Limiting access to protect cloud storage. Using secrets for managing and sharing sensitive information. Monitoring cloud resources for threat detection.