> 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-367.md).

# AWS Sagemaker data quality job not encrypting model artifacts with KMS misconfiguration detected in

## Rule Details

|                        |                                           |
| ---------------------- | ----------------------------------------- |
| Cortex AppSec Rule ID  | APPSEC\_AWS\_367                          |
| 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 model artifacts. Unencrypted model artifacts pose a significant security concern. Without encryption, sensitive information within your models could be exposed to malicious actors. This could lead to data breaches, compromising the integrity and confidentiality of the data used by your models. KMS provides a secure solution for managing encryption keys, ensuring the protection of your machine learning assets.

## How to Fix

*Resource:* aws\_sagemaker\_data\_quality\_job\_definition

* *Arguments:* data\_quality\_job\_output\_config/kms\_key\_id

Encrypt your AWS SageMaker Data Quality Job output. Specify the KMS key in the data\_quality\_job\_output\_config block of your Terraform configuration file, as displayed in the example below.

## \[source,go]

resource "aws\_sagemaker\_data\_quality\_job\_definition" "example" { ...

data\_quality\_job\_output\_config {

* kms\_key\_id = "arn:aws:kms:us-west-2:123456789012:key/my-existing-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-367.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.