> 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/appsec2-gcp-25.md).

# Vertex AI featurestore is not configured to use a Customer Managed Key (CMK) misconfiguration detect

## Rule Details

|                        |                           |
| ---------------------- | ------------------------- |
| Cortex AppSec Rule ID  | APPSEC2\_GCP\_25          |
| Category - Subcategory | Storage - Encryption      |
| Provider               | GCP                       |
| Severity               | MEDIUM                    |
| Framework              | Terraform, Terraform Plan |

## Impact

This rule is examining whether a Vertex AI feature store is using a Customer Managed Key (CMK) for encryption. It is highly important to follow this rule because using a CMK provides greater control over the keys used for encryption. If a default key is used, it could potentially be more vulnerable to certain types of risks such as unauthorized access. With a Customer Managed Key, the user has complete control over the key's rotation, disablement, and deletion, which significantly improves data security.

## How to Fix

*Resource:* google\_vertex\_ai\_featurestore

* *Arguments:* encryption\_spec

To fix this issue, you need to designate a Customer-Managed Encryption Key (CMEK) for your GCP Vertex AI feature store. You can do this by specifying the "encryption\_spec" block and pointing to your Customer-Managed Key in your terraform resource for the feature store.

## The updated Terraform code now includes the "encryption\_spec" configuration, which is set to utilize a Customer-Managed Encryption Key. Now, the feature store data is encrypted using the customer-managed key which enhances the security of sensitive data, as the customer has direct control over key management activities. \[source,go]

## data "google\_vertex\_ai\_featurestore" "example" { ... encryption\_spec { kms\_key\_name = "some\_name" } ... }


---

# 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/appsec2-gcp-25.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.