Automated AI Discovery: Amazon SageMaker Integration for AI Governance Models

Overview

The Amazon SageMaker MLOps Discovery wizard allows you to continuously scan, detect, and inventory AI models and agents across your AWS-native ecosystem. This seamless integration experience enables real-time visibility into your organization's AI landscape, mapping relationships between models, training data, and business processes to strengthen risk management and compliance. By eliminating manual tracking, teams can focus on innovation while maintaining comprehensive oversight of their AI systems through automated discovery and continuous monitoring.

Reduce manual data entry

The integration automates the synchronization of model attributes between Amazon SageMaker and OneTrust, eliminating the need for manual data entry. This can save you time and effort and help reduce errors.

Maintain up-to-date model information

The integration ensures that your model information is always up-to-date in OneTrust. This is important for compliance and risk management purposes.

Preserve the integrity of data

The integration allows you to configure a non-overwrite policy, which means that data from Amazon SageMaker will not overwrite data in OneTrust.

To create the integration

  1. In the Integrations menu, select Gallery.

    The Integration Gallery screen appears.

  2. Use the search function to locate and select the Amazon SageMaker tile.

    The Gallery Details screen appears.


    Integrations gallery - Amazon SageMaker
  3. Click the Add button for the Amazon SageMaker MLOps Discovery wizard.

    The Workflow Wizard screen appears.


    Amazon SageMaker MLOps Discovery tile
  4. In the Name your new connection section, provide a unique name for the connection and a notification email.

    Integration wizard - Amazon SageMaker MLOps Discovery new integration
  5. In the Provide Login Credentials section, select existing Cloud SDK credentials or create a new one here with your AWS
    Existing

    Provide Login Credentials section - Select existing credentials
    Create New

    Provide Login Credentials section - Create new credentials
    Field
    Description
    Configuration type

    Defaults to AWS

    Application type

    Defaults to Cloud SDK

    Cloud platform

    Defaults to AWS

    Hostname

    Provide the hostname to your AWS account.

    AWS access key

    Provide your access key from your AWS account.

    AWS secret key

    Provide your secret key from your AWS account.

    AWS service name

    Provide your service name from your AWS account.

    AWS region

    Provide the region where the the AWS account resides.

  6. Click the Next button.

    The Configure Trigger screen appears.

  7. Define how often this integration should trigger with the Frequency and Date and Time options.

    • Run Manually

    • Hourly

    • Daily

    • Weekly

    • Monthly

    • Yearly

  8. Click the Next button.
  9. Provide your given Host and Region in the corresponding sections. These must be consistent with SageMaker to sync the correct model records.

    Host and Region sections
  10. Click the Next button.

    The Integration Summary screen appears.

  11. Review your Amazon SageMaker integration settings. Confirm that these details are correct before activating the workflow.
  12. Click the Save button. The screen will populate with the last updated update and post an Inactive status.
  13. Click the Activate button. A confirmation modal will appear for you click the Ok button.
  14. The integration is now active and ready to trigger and sync your model information.