> 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/cortex-xsiam/configure-cortex-xsiam/cortex-xsiam-data-sources/vendor-specific-data-sources-and-connectors/google/google-ai.md).

# Google AI

Use Google AI data with Cortex XSIAM.

{% hint style="warning" %}
**Important**

This connector is only available for tenants that onboarded after July 26, 2026. For tenants that onboarded before this date, use Marketplace to access the standalone integration. For more information, see [Marketplace](/cortex-xsiam/configure-cortex-xsiam/marketplace.md).
{% endhint %}

This connector is available with any active Cortex XSIAM or Cortex AgentiX license.

The Google Gemini connector provides access to Google's advanced large language models for AI-powered chat conversations, text analysis, and natural language processing within Cortex XSOAR / XSIAM. It supports two authentication modes: Google AI Studio (API key) and Google Cloud Vertex AI (service account), and multiple Gemini models.

This connector includes the following sub-capabilities (Marketplace integrations link to PAN DEV for more information):

* [GoogleGemini](https://xsoar.pan.dev/docs/reference/integrations/google-gemini): Google Gemini LLM Integration for AI-powered analysis and chat capabilities. This integration provides access to Google Gemini's large language models for AI-powered chat conversations, text analysis and generation, and natural language processing tasks.

To configure this connector, follow the steps outlined in the configuration wizard.


---

# Agent Instructions
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## 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/cortex-xsiam/configure-cortex-xsiam/cortex-xsiam-data-sources/vendor-specific-data-sources-and-connectors/google/google-ai.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.