> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/appsec-rules/iac-security/ai-and-machine-learning/appsec-aws-368.md).

# AWS Sagemaker data quality job not using KMS to encrypt data on attached storage volume misconfigura

## Rule Details

|                        |                                           |
| ---------------------- | ----------------------------------------- |
| Cortex AppSec Rule ID  | APPSEC\_AWS\_368                          |
| Category - Subcategory | Storage - Encryption                      |
| Provider               | AWS                                       |
| Severity               | LOW                                       |
| Framework              | CloudFormation, Terraform, Terraform Plan |

## Impact

This rule checks whether Amazon SageMaker Data Quality Jobs leverage AWS Key Management Service (KMS) to encrypt data on the attached storage volume. KMS encryption is crucial to safeguard sensitive information as it encrypts data on storage volumes attached to the Sagemaker instance. This prevents unauthorized access and data breaches and helps ensure compliance with regulations requiring the protection of specific data types.

## How to Fix

*Resource:* AWS::SageMaker::DataQualityJobDefinition

* *Arguments:* JobResources/ClusterConfig/VolumeKmsKeyId

## Provide a `VolumeKmsKeyId` attribute in your `AWS::SageMaker::DataQualityJobDefinition` of your CloudFormation configuration file, as displayed in the example below. \[source,go]

## MyDataQualityJobDefinitionPass: Type: "AWS::SageMaker::DataQualityJobDefinition" Properties: ... JobResources: ClusterConfig: ... VolumeKmsKeyId: "arn:aws:kms:us-west-2:123456789012:key/my-existing-volume-kms-key-id"


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://cortex-docs.paloaltonetworks.com/appsec-rules/iac-security/ai-and-machine-learning/appsec-aws-368.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
