1.3.1. Shared Responsibility: AWS's Role (ML Focus)

First Principle: AWS is responsible for "security of the cloud," protecting the underlying global infrastructure, hardware, software, and physical facilities that deliver ML services like Amazon SageMaker.

In the AWS Shared Responsibility Model, AWS's responsibility is to protect the global infrastructure that runs all of the services offered in the AWS Cloud. This "security of the cloud" means AWS manages and controls the physical security of data centers, the underlying compute, storage, and networking hardware, and the virtualization layer.

Key AWS Responsibilities ("Security of the Cloud") for ML:

• Physical Security: Data centers where ML resources are hosted (e.g., SageMaker instances, S3 storage).
• Global Infrastructure: The underlying compute, storage, and networking that powers Amazon SageMaker, AWS Glue, Amazon EMR, Amazon S3, etc.
• Managed Service Infrastructure: This includes the underlying hosts, operating systems, and network configurations for services like:
  • SageMaker (managed training environments, hosting endpoints).
  • AWS Glue (serverless ETL infrastructure).
  • Amazon S3 (object storage durability and availability).
  • Amazon DynamoDB (NoSQL database infrastructure).
  • Amazon Redshift (data warehouse infrastructure).
• Patching and Security Configuration: For the underlying infrastructure managed by AWS for these ML-related services.

Scenario: You are an ML specialist building a model on Amazon SageMaker. You're concerned about the security of the virtual machines hosting your SageMaker notebooks and training jobs, and the physical security of the data centers.

Reflection Question: How does AWS's "security of the cloud" responsibility, by managing the underlying infrastructure and physical security of its global data centers and managed ML services, enable you as an ML Specialist to focus on designing and configuring your ML workflows within the cloud rather than the foundational infrastructure?