AI Service Graph Connector for GCP Vertex AI

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of AI Service Graph Connector for GCP Vertex AI

    The AI Service Graph Connector for GCP Vertex AI allows ServiceNow customers to discover and import AI assets from their Google Cloud environment into the ServiceNow AI Control Tower. This integration catalogs AI systems, agents, models, and prompts from Google Cloud Platform (GCP), automatically collecting usage data. This data populates the AI Control Tower’s value dashboard, offering enhanced visibility and governance of AI operations within your enterprise.

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    Supported Versions and Roles

    • The connector is supported on ServiceNow releases: Australia, Zurich, and Yokohama.
    • Required user roles include snaidisc.discoveryadmin and sncmdbintutil.sgcadmin to configure and manage the connector.

    ServiceNow Prerequisites

    Initial setup requires these key steps:

    • Update Data Source Access: Grant write permissions on the Data Source table by enabling create, update, and delete permissions in the Global scope, then switch back to the connector’s application scope.
    • Clear Cache: Run a background script to flush cached data for Data Source and Table definitions. This ensures that changes take effect properly and may take several minutes to complete.

    GCP Vertex AI Prerequisites

    • Create a service account in GCP, assign and bind appropriate roles, and enable necessary APIs.
    • Prepare authentication files: a JSON key file is required; if available, you can skip creating a JKS file.
    • Register the connector within your ServiceNow instance following detailed setup instructions.

    Enabling Cloud Tracing

    To fully capture detailed AI operational data such as prompts, tools, models, and sub-agents, cloud tracing must be enabled in GCP. Without it, only top-level agent names are visible. Enabling tracing and redeploying agents is essential for comprehensive AI asset discovery and monitoring.

    Data Mapping and Storage

    The connector organizes imported GCP Vertex AI data through specific data sources, staging tables, and target tables within ServiceNow’s CMDB and related classes. This structure supports detailed asset management and usage tracking:

    • Execution Data: Captured in usage tables.
    • AI Systems and Components: Mapped to CMDB AI system and product model classes.
    • Models, Tools, and Prompts: Stored in corresponding product model and digital asset tables.
    • System Subcomponents: Managed via many-to-many relationship tables to reflect complex AI system relationships.

    What Customers Can Expect

    By integrating GCP Vertex AI with ServiceNow AI Control Tower through this connector, customers gain automated, comprehensive discovery and governance of AI assets across their cloud environment. This enables better visibility into AI usage, supports compliance and operational oversight, and helps optimize AI resource management within the enterprise.

    The AI Service Graph Connector for GCP Vertex AI enables you to discover and import AI assets from your Google Cloud environment into ServiceNow AI Control Tower.

    The connector integrates with your Google Cloud Platform account to catalog AI systems, agents, models, and prompts. Usage data is automatically collected and populated into the AI Control Tower value dashboard, providing comprehensive visibility and governance of your AI operations.

    Download apps from the Store

    Visit the  ServiceNow store website to download the AI Service Graph Connector for GCP Vertex AI application.

    Supported ServiceNow versions

    This connector is supported on the following ServiceNow releases:

    Release Status
    Australia Supported
    Zurich Supported
    Yokohama Supported

    User Roles

    You must have one of the following roles assigned.

    Required Roles
    sn_ai_disc.discovery_admin
    sn_cmdb_int_util.sgc_admin

    ServiceNow Prerequisites

    Complete the following setup steps once when configuring the connector for the first time.

    Note:
    Updating data source access and clear cache is a prerequisite that needs to be completed only once, when setting up a new instance for the first time.
    Update Data Source Access

    The connector requires write permissions to the Data Source table to create data sources.

    To enable data source creation:
    1. Select Global from the application picker.
    2. Navigate to Application Access.
    3. Select the Can create, Can update, and Can delete checkboxes.
    4. Select Update.
    5. Switch to the connector application scope.
    Clear cache

    Clear the cached data for the Data Source and Tables.

    To clear the cache:
    1. Navigate to System Definition > Background Scripts
    2. Paste the following script into the Run Script text box:
      GlideTableManager.invalidateTable('sys_data_source');
GlideCacheManager.flushTable('sys_data_source');
GlideTableManager.invalidateTable('sys_db_object');
GlideCacheManager.flushTable('sys_db_object');
    3. Select Run Script.
      Note:
      The script may take several minutes to complete.
    4. After completion, switch to the connector application scope.

    GCP Vertex AI Prerequisites

    Follow the setup instructions to create a service account, assign roles, bind roles to the service account, and enable APIs. To create a JKS file, a JSON file is required. If a JSON file is available, skip the JKS file creation step. After completing setup, register the connector in your ServiceNow instance. For setup instructions and API details, see the Service Graph connector for GCP Vertex AI- Setup Instructions [KB2731256] KB article.

    Enable Cloud Tracing

    • If cloud tracing is not turned on, we will only be able to view the reasoning engine name, which is the top-level agent.
    • Cloud tracing must be enabled to capture details such as prompts, tools, model, and sub-agents. These details are identified only after they have been executed at least once.
    • Agent tracing is currently inactive. We need to enable tracing and redeploy the agents to properly discover AI agents, tools, model, and sub-agents

    Data Mapping

    The following table lists the data sources, the staging tables, and the target tables  CMDB CI classes and non-CMDB classes where data is stored for a  GCP Vertex AI  project.

    Table 1. Data sources, staging tables, and target tables
    Data source Staging table Target tables
    SG-GCPVertexAI-Execution sn_ai_disc_gcp_sgc_sg_gcp_execution sn_ai_disc_ai_usage
    SG-GCPVertexAI-System sn_ai_disc_gcp_sgc_sg_gcp_ai_system cmdb_ai_system_component_product_model

    alm_ai_system_digital_asset

    cmdb_ci_function_ai

    cmdb_rel_asset_ci
    SG-GCPVertexAI-Model sn_ai_disc_gcp_sgc_sg_gcp_ai_model cmdb_ai_model_product_model

    alm_ai_model_digital_asset
    SG-GCPVertexAI-Tool sn_ai_disc_gcp_sgc_sg_gcp_ai_tool sn_ent_ai_tool
    SG-GCPVertexAI-Prompt sn_ai_disc_gcp_sgc_sg_gcp_ai_prompt cmdb_ai_prompt_product_model

    alm_ai_prompt_digital_asset
    SG-GCPVertexAI-System Subcomponent M2M sn_ai_disc_gcp_sgc_sg_gcp_ai_system_subcomponent_m2m sn_ent_ai_system_subcomponent_m2m