Hardening a Cleanroom AI Platform to allow model training & inference on Protected Health Information

Artificial intelligence projects in high-compliance industries, like healthcare and life science, often require processing Protected Health Information (PHI). This may happen because the nature of the projects does not allow full de-identification in advance – for example, when dealing with rare diseases, genetic sequencing data, identify theft, or training de-identification models – or when training is anonymized data but inference must happen on data with PHI.

In such scenarios, the alternative is to create an “AI cleanroom” – an isolated, hardened, air-gap environment where the work happens. Such a software platform should enable data scientists to log into the cleanroom, and do all the development work inside it – from initial data exploration & experimentation to model deployment & operations – while no data, computation, or generated assets ever leave the cleanroom.

This webinar presents the architecture of such a Cleanroom AI Platform, which has been actively used by Fortune 500 companies for the past three years. Second, it will survey the hundreds of DevOps & SecOps features requires to realize such a platform – from multi-factor authentication and point-to-point encryption to vulnerability scanning and network isolation. Third, it will explain how a Kubernetes-based architecture enables “Cleanroom AI” without giving up on the main benefits of cloud computing: elasticity, scalability, turnkey deployment, and a fully managed environment.