> 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/public-exposure/appsec-aws-306.md).

# AWS SageMaker notebook instance is not placed in VPC misconfiguration detected in code

## Rule Details

|                        |                                      |
| ---------------------- | ------------------------------------ |
| Cortex AppSec Rule ID  | APPSEC\_AWS\_306                     |
| Category - Subcategory | Public Exposure - VPC VCN VNet       |
| Provider               | AWS                                  |
| Severity               | LOW                                  |
| Framework              | Terraform, Terraform Plan            |
| Mapped CSPM/KSPM Rule  | e776dc78-2f1a-4b19-b86f-79eeec8c90c0 |

## Impact

If a SageMaker notebook instance is not launched into a custom VPC, it might be exposed to security vulnerabilities. This exposure could arise from traffic not being restricted to a confined, secure environment. An instance launched outside of a custom VPC might also be at risk of unwanted access from unknown entities, which could lead to potential data breaches or unauthorized changes.

Therefore, launching instances into a custom VPC helps in the prevention of unauthorized access, minimizes attack surface, and provides a more secure and controlled environment. It helps safeguard valuable data and promotes safe business practices.

## How to Fix

*Resource:* aws\_sagemaker\_notebook\_instance

* *Arguments:* subnet\_id

## To fix this issue, you need to specify a `subnet_id` to create your SageMaker Notebook instance within a pre-defined VPC. Here is an example: \[source,go]

resource "aws\_sagemaker\_notebook\_instance" "notebook" { name = "notebook" role\_arn = aws\_iam\_role.role.arn

* subnet\_id = aws\_subnet.example.id # This launches it within a custom VPC instance\_type = "ml.t2.medium"

## lifecycle\_config\_name = aws\_sagemaker\_notebook\_instance\_lifecycle\_configuration.lifecycle\_configuration.name }


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