> 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-370.md).

# AWS SageMaker model does not use network isolation misconfiguration detected in code

## Rule Details

|                        |                                           |
| ---------------------- | ----------------------------------------- |
| Cortex AppSec Rule ID  | APPSEC\_AWS\_370                          |
| Category - Subcategory | Public Exposure - VPC VCN VNet            |
| Provider               | AWS                                       |
| Severity               | MEDIUM                                    |
| Framework              | CloudFormation, Terraform, Terraform Plan |

## Impact

This rule checks whether Amazon SageMaker models leverage network isolation. Network isolation restricts unauthorized network communication between computing resources, enhancing security. When enabled for SageMaker models, it ensures inference code runs in an internet-free environment, safeguarding your models and data sets from potential breaches. A violation of this rule indicates that the SageMaker model's network isolation is disabled, potentially exposing your models and data to security threats. Enabling network isolation strengthens your security posture and protects your valuable assets.

## How to Fix

*Resource:* AWS::SageMaker::Model

* *Arguments:* Properties/EnableNetworkIsolation

## Enable the network isolation parameter within the AWS::SageMaker::Model resource in your CloudFormation template. This restricts unauthorized network access and strengthens your security posture. \[source,go]

Resources: SageMakerModelPass: Type: 'AWS::SageMaker::Model' Properties: ...

* EnableNetworkIsolation: True

***


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://cortex-docs.paloaltonetworks.com/appsec-rules/iac-security/ai-and-machine-learning/appsec-aws-370.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.