Amazon SageMaker Studio is a web-based fully integrated development environment (IDE) where you can perform end-to-end machine learning (ML) development to prepare data and build, train, and deploy models.
Like other AWS services, Studio supports a rich set of security-related features that allow you to build highly secure and compliant environments.
One of these fundamental security features allows you to launch Studio in your own Amazon Virtual Private Cloud (Amazon VPC). This allows you to control, monitor, and inspect network traffic within and outside your VPC using standard AWS networking and security capabilities. For more information, see Securing Amazon SageMaker Studio connectivity using a private VPC.
Customers in regulated industries, such as financial services, often don’t allow any internet access in ML environments. They often use only VPC endpoints for AWS services, and connect only to private source code repositories in which all libraries have been vetted both in terms of security and licensing. Customers may want to provide internet access but also have some controls such as domain name or URL filtering and allow access to only specific public repositories and websites, possibly packet inspection, or other network traffic-related security controls. For these cases, AWS Network Firewall and NAT gateway-based deployment may provide a suitable use case.
In this post, we show how you can use Network Firewall to build a secure and compliant environment by restricting and monitoring internet access, inspecting traffic, and using stateless and stateful firewall engine rules to control the network flow between Studio notebooks and the internet.
Depending on your security, compliance, and governance rules, you may not need to or cannot completely block internet access from Studio and your AI and ML workloads. You may have requirements beyond the scope of network security controls implemented by security groups and network access control lists (ACLs), such as application protocol protection, deep packet inspection, domain name filtering, and intrusion prevention system (IPS). Your network traffic controls may also require many more rules compared to what is currently supported in security groups and network ACLs. In these scenarios, you can use Network Firewall—a managed network firewall and IPS for your VPC.
When you deploy Studio in your VPC, you control how Studio accesses the internet with the parameter
AppNetworkAccessType (via the Amazon SageMaker API) or by selecting your preference on the console when you create a Studio domain.
If you select Public internet Only (
PublicInternetOnly), all the ingress and egress internet traffic from Amazon SageMaker notebooks flows through an AWS managed internet gateway
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/securing-amazon-sagemaker-studio-internet-traffic-using-aws-network-firewall/