Understanding security and privacy-preserving ML
ML models often rely on vast amounts of data, including potentially sensitive information about individuals, such as personal details, financial records, medical histories, or browsing behavior. The improper handling or exposure of this data can lead to serious privacy breaches, putting individuals at risk of discrimination, identity theft, or other harmful consequences. To ensure compliance with data privacy regulations or even internal data privacy controls, ML systems need to provide foundational infrastructure security features such as data encryption, network isolation, compute isolation, and private connectivity. With a SageMaker-based ML platform, you can enable the following key security controls:
- Private networking: As SageMaker is a fully managed service, it runs in an AWS-owned account. By default, resources in your own AWS account communicate with SageMaker APIs via the public internet. To enable private connectivity...
