Highly-regulated industries, such as financial services, are often required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks.
This post walks you through the steps to implement access control and auditing capabilities on a per-user basis, using Amazon SageMaker Studio notebooks and AWS Lake Formation access control policies. This is a how-to guide based on the Machine Learning Lens for the AWS Well-Architected Framework, following the design principles described in the Security Pillar:
Restrict access to ML systems
Ensure data governance
Enforce data …
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