1.3. AWS Shared Responsibility Model (ML Context)

At its core, the AWS Shared Responsibility Model is a fundamental principle clarifying security obligations in the cloud. Its core purpose is to define precisely who is accountable for what aspects of security, ensuring no gaps in protection. For Machine Learning Specialists, understanding this model is crucial to effectively manage the security posture of their ML workloads.

AWS is responsible for "security of the cloud", encompassing the underlying infrastructure that runs ML services. Conversely, the customer (including ML Specialists) is responsible for "security in the cloud", covering everything configured and managed within their AWS ML environment.

Understanding this distinction is paramount for the AWS MLS-C01 exam. It directly impacts how you design, implement, and operate ML solutions, from data storage to model deployment and access control. Misinterpreting these roles can lead to significant ML security vulnerabilities or operational issues.

Scenario: You are an ML specialist designing a data lake for ML training. You're trying to determine if you are responsible for the physical security of the servers hosting Amazon S3 or the encryption settings for your S3 buckets.

Reflection Question: How does understanding the AWS Shared Responsibility Model clarify your role as an ML Specialist in securing your cloud ML workloads (e.g., data encryption, IAM policies) versus AWS's responsibility for the underlying ML service infrastructure?