Secure deployment of Amazon SageMaker resources

Amazon SageMaker, like other services in Amazon Web Services (AWS), includes security-related parameters and configurations that you can use to improve the security posture of resources as you deploy them. However, many of these security-related parameters are optional, allowing you to deploy resources without them. While this might be acceptable in the initial exploration stage, customers want resources to be deployed more securely in production.

In this post I will discuss three approaches for deploying Amazon SageMaker resources more securely and highlight some pros and cons with each approach.

Before you begin

This post assumes general familiarity with machine learning and Amazon SageMaker. In addition, it assumes knowledge of the services used to implement security controls, including:

Approaches

Amazon SageMaker contains security-related parameters for the secure deployment of resources within it. For example, when creating an Amazon SageMaker notebook instance, root access on the notebook instance can be disabled. Another example is when creating an Amazon SageMaker training job, it can be set up to access other services like Amazon Simple Storage Service (Amazon S3) through an endpoint in the customer’s Amazon Virtual Private Cloud (Amazon VPC).

However, these and most other security-related parameters and configurations are optional. As examples of less-secure configuration, Amazon SageMaker notebook instances can be created with root access enabled, and training jobs can access Amazon S3 over the public endpoints.

There are two main methods of implementing controls to improve the security of AWS services during deployment. One of them is preventive and uses controls to stop an event from occurring. The other is responsive, and uses controls that are applied in response to events.

Preventive controls protect workloads and mitigate threats and vulnerabilities. A couple of approaches to implement preventive controls are:

  • Use IAM condition keys supported by the service to ensure that resources without necessary security controls cannot be deployed.
  • Use the AWS Service Catalog to invoke AWS CloudFormation templates that deploy resources with all the necessary security controls in place.

Responsive controls drive remediation of potential deviations from security baselines. An approach to implement responsive controls is:

  • Use CloudWatch Events to catch resource creation events, then use a Lambda function to validate that resources were deployed with the necessary security controls, or terminate resources any if the necessary security controls aren’t present.

The next few sections talk about each of these approaches in respect to Amazon SageMaker.

IAM condition keys approach

IAM condition keys can be used to improve security by preventing resources from being created without security controls. When a principal makes an API request to AWS to create a resource, the request information is gathered into a request context. This request context is compared to conditions in the principal’s policy. If the conditions pass, the API request is allowed to proceed and the resource will be created. However, if the conditions fail, the API request is stopped and the resource won’t be created.

The optional Condition element (or block) in an IAM policy is where expressions are built using condition operators (such as StringEquals or NumericLessThan). These condition expressions match the condition keys and values in the policy to the keys and values in the request context. The condition key specified in a condition element can be global or service-specific.

A condition element has the following syntax:

"Condition": { "{condition-operator}": { "{condition-key}": "{condition-value}" } }

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