GoogleGemini

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 - Natural language processing tasks Supports both Google AI Studio (API key) and Google Cloud Vertex AI (service account) authentication. Supported models include Gemini 2.0 Flash, Gemini 1.5 Pro, and various preview models.

Cloud Services · Google Gemini

Details

IDGoogleGemini
ProviderGoogle
CategoryCloud Services
From Version6.10.0
Docker Imagedemisto/google-api-py3:1.0.0.10182333
Supported ModulesXSIAM Agentix

README

Google Gemini Integration

This integration provides access to Google Gemini’s large language models for AI-powered analysis and chat capabilities in Cortex XSOAR or XSIAM. Supports both Google AI Studio (API key) and Google Cloud Vertex AI (service account) authentication.

Configure GoogleGemini in Cortex XSOAR

  1. Navigate to Settings > Integrations > Servers & Services.
  2. Search for Google Gemini.
  3. Click Add instance to create and configure a new integration instance.

Configure GoogleGemini in Cortex XSIAM

  1. Go to Marketplace
  2. Search for GoogleGemini
  3. Add ContentPack
  4. Search for GoogleGemini in Data Source and Integrations
  5. Create new instance

Instance Configuration Parameters

Parameter Description Required
Authentication Type Choose between “AI Studio API Key” or “Vertex AI Service Account” True
Server URL For AI Studio: https://generativelanguage.googleapis.com. For Vertex AI: https://aiplatform.googleapis.com (auto-detected if unchanged). True
API Key Google AI Studio API key. Required when using AI Studio. False
Service Account Key (JSON) Service Account Key JSON for Vertex AI authentication. Required when using Vertex AI. False
Project ID Google Cloud Project ID. Required when using Vertex AI. False
Location Google Cloud location for Vertex AI (e.g., global, us-central1). Defaults to global. False
Default Model Select a Gemini model from the dropdown True
Max tokens Maximum number of tokens in the response (default: 1024) True
Temperature Controls randomness in responses (0.0-2.0) False
Top P Nucleus sampling parameter (0.0-1.0) False
Top K Top-k sampling parameter False
Trust any certificate (not secure) Whether to ignore SSL certificate verification False
Use system proxy settings Whether to use system proxy configuration False

Supported Models

The integration supports various Gemini models including:

Stable Models:

  • gemini-2.5-pro
  • gemini-2.5-flash

Deprecated (Legacy Only) Models — operational until June 1, 2026:

  • gemini-2.0-flash
  • gemini-2.0-flash-lite

Preview Models:

  • gemini-3.1-pro-preview
  • gemini-3.1-flash-preview
  • gemini-3.1-flash-lite

Note: You can also use the freetext model field to specify newer models not in the dropdown list.

Commands

You can execute these commands from the CLI, as part of an automation, or in a playbook.
After you successfully execute a command, a DBot message appears in the War Room with the command details.

google-gemini-send-message

Send a prompt to Google Gemini and receive an AI-generated response.

Base Command

google-gemini-send-message

Input

Argument Name Description Required
prompt The prompt or question to send to the AI model Required
model Override the instance default model for this specific request Optional
history Conversation history in JSON format for maintaining context across multiple interactions Optional
save_conversation Whether to automatically save and retrieve conversation history (default: false) Optional

Context Output

Path Type Description
GoogleGemini.Chat.Prompt String The original prompt sent to the model
GoogleGemini.Chat.Response String The AI model’s response
GoogleGemini.Chat.Model String The model used for generation
GoogleGemini.Chat.Temperature Number The temperature parameter used for response generation
GoogleGemini.Chat.History Array Complete conversation history (when save_conversation=true)
GoogleGemini.Chat.ConversationId String A unique identifier, used to identify the chat session

Command Examples

!google-gemini-send-message prompt="What is artificial intelligence?"

!google-gemini-send-message prompt="Analyze this suspicious email for potential threats" model="gemini-2.5-pro"

!google-gemini-send-message prompt="Continue our previous discussion" history='[{"role": "user", "parts": [{"text": "Hello"}]}, {"role": "model", "parts": [{"text": "Hi there! How can I help you?"}]}]'

!google-gemini-send-message prompt="What are the next investigation steps?" save_conversation=true

Conversation History Management

When save_conversation=true, the integration:

  • Automatically retrieves existing conversation history from context
  • Uses the last exchange (user + model response) to provide context for the current request
  • Saves the complete updated conversation history to GoogleGemini.Chat.History
  • Allows analysts to maintain conversation continuity without manually managing JSON history

Human Readable Output

The command returns the AI model’s response as human-readable output in the War Room.

Setup Instructions

AI Studio (API Key)

  1. Obtain API Key: Visit Google AI Studio to create an API key.
  2. Configure Integration: Add a new GoogleGemini integration instance, set Authentication Type to AI Studio API Key, and enter your API key.
  3. Test Connection: Use the Test button to verify connectivity.
  4. Start Using: Execute the google-gemini-send-message command for AI interactions.

Vertex AI (Service Account)

  1. Create a Service Account: In the Google Cloud Console, go to IAM & Admin > Service Accounts and create a service account with the Vertex AI User role.
  2. Generate a JSON Key: On the service account page, create a new JSON key and download it.
  3. Configure Integration: Add a new GoogleGemini integration instance, set Authentication Type to Vertex AI Service Account, and paste the full JSON key contents into the Service Account Key field.
  4. Set Project ID: Enter your Google Cloud Project ID.
  5. Set Location: Enter the location (default: global). Use us-central1, europe-west4, etc. for regional endpoints.
  6. Test Connection: Use the Test button to verify connectivity.

Troubleshooting and Tips

  • Ensure your API key has access to the Generative Language API.
  • Verify your Cortex XSOAR or XSIAM instance can access the configured endpoint.
  • Check that the specified model is available in your region.
  • Review usage quotas and rate limits for your API key or project.
  • The integration attempts to use models not included in the official list and issues a warning.
  • Ensure the service account has the roles/aiplatform.user role and the Vertex AI API is enabled in your project.
  • For AI Studio, use the server URL https://generativelanguage.googleapis.com. For Vertex AI, the URL auto-switches to https://aiplatform.googleapis.com by default.

Configuration parameters

  • auth_type — Authentication Type (required)
  • url — Server URL (required)
  • api_key —
  • service_account_key —
  • project_id — Project ID
  • location — Location
  • model — Default Model (required)
  • max_tokens — Max tokens (required)
  • temperature — Temperature
  • top_p — Top P
  • top_k — Top K
  • insecure — Trust any certificate (not secure)
  • proxy — Use system proxy settings

Commands (1)

  • google-gemini-send-message

    Send a chat message to the Gemini AI model.

Source

GoogleGemini.py GoogleGemini_test.py GoogleGemini.yml GoogleGemini_description.md
Download History Raw 
# Google Gemini Integration

The Google Gemini integration provides seamless access to Google's advanced large language models from within Cortex XSOAR / XSIAM, empowering analysts with intelligent AI assistance for investigation workflows and decision-making processes. This integration enables AI-powered chat conversations, text analysis, and natural language processing capabilities for enhanced security automation. Multiple Gemini models are supported, including 2.0 Flash and 1.5 Pro variants.

This integration supports two authentication modes:
- **AI Studio API Key**: For use with [Google AI Studio](generativelanguage.googleapis.com).
- **Vertex AI Service Account**: For use with [Google Cloud Vertex AI](aiplatform.googleapis.com).


## AI Studio

1. In [Google AI Studio](https://makersuite.google.com/app/apikey), create an API key.
2. In Cortex XSOAR / XSIAM, add a new GoogleGemini integration instance.  
3. Set **Authentication Type** to `AI Studio API Key`, and enter your API key.
4. Click **Test** to verify the connectivity.
4. Run the `google-gemini-send-message` command for AI interactions.

## Vertex AI

1. In the Google Cloud Console, go to **IAM & Admin** > **Service Accounts** and create a service account with the Vertex AI User role.
2. On the service account page, create a new JSON key and download it.
3. In Cortex XSOAR / XSIAM, add a new GoogleGemini integration instance.  
4. Set **Authentication Type** to `Vertex AI Service Account` and paste the full JSON key contents into the **Service Account Key** field.
5. Enter your Google Cloud **Project ID**.
5. **Set Location**: Enter the location (default: `global`). Use `us-central1`, `europe-west4`, etc. for regional endpoints.
7. Click **Test** to verify connectivity.

## Troubleshooting and Tips

- Ensure your API key has access to the Generative Language API.
- Verify your Cortex XSOAR / XSIAM instance can access the configured endpoint.
- Check that the specified model is available in your region.
- Review usage quotas and rate limits for your API key or project.
- The integration attempts to use models not included in the official list and issues a warning.
- Ensure the service account has the `roles/aiplatform.user` role and the Vertex AI API is enabled in your project.
- **Server URL**: For AI Studio, use `https://generativelanguage.googleapis.com`. For Vertex AI, the URL auto-switches to `https://aiplatform.googleapis.com` if left at the default.