To support security and compliance needs, you may need granular control over how shared ML features are accessed. These needs often go beyond table and column-level access control to individual row-level access control. For example, you may want to let account representatives see rows from a sales table for only their accounts and mask the prefix of sensitive data like credit card numbers. SageMaker Feature Store together with AWS Lake Formation can be used to implement fine-grained access controls to protect feature store data and grant access based on role.