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Overview In modern Machine Learning Operations (MLOps), the speed of innovation often outpaces the implementation of foundational security controls. One of the most critical yet overlooked areas is the network security of the training environment itself. Amazon SageMaker, a cornerstone of ML development on AWS, uses containers to run training jobs. By default, these containers […]

Overview As organizations increasingly rely on machine learning (ML) to drive business outcomes, the security of the underlying infrastructure becomes a critical governance concern. In the AWS ecosystem, Amazon SageMaker provides a powerful platform for building, training, and deploying ML models. However, the containers that run these models can also introduce new attack vectors if […]

Overview In modern machine learning operations, distributed training jobs are essential for building complex models at scale. Amazon SageMaker facilitates this by spreading workloads across multiple compute instances, but this architecture introduces a significant security risk. By default, the communication between these instances, or containers, is unencrypted. This leaves sensitive data, such as model weights […]

Overview As organizations increasingly rely on machine learning (ML) models for critical business functions, the security and operational visibility of these systems become paramount. In the AWS ecosystem, Amazon SageMaker provides a robust platform for deploying ML models as real-time inference endpoints. However, without proper configuration, these endpoints can operate as “black boxes,” processing data […]

Overview In modern AWS environments, secure and auditable administrative access has shifted away from traditional SSH and RDP protocols. AWS Systems Manager (SSM) Session Manager is now the standard for connecting to compute resources like Amazon EC2 instances without exposing inbound ports, significantly reducing the attack surface. While this method enhances security, it introduces a […]