AI Governance - April 25, 2026 - 202604.2.0

|Available with Solution Packaging Only

Establish standardized global records for AI systems, models, and datasets while maintaining contextual, local records across business units, use cases, and lifecycle stages. This capability lets teams track AI asset evolution through a clear audit trail, enforce standardized attributes at the global level, and manage jurisdiction-specific or use case-specific requirements at the local level.

For more information, see AI Governance Global/Local Feature Guide. This feature is related to the following ideas: AI-I-31, PDGI-I-1416

|Available with Solution Packaging Only

Automatically scan cloud environments, APIs, pipelines, and applications to detect active AI models and agents in your Amazon Bedrock accounts. This feature captures metadata including model type, owners, input/output behavior, and associated datasets, providing a real-time, searchable inventory that serves as the foundation for governance policies, risk assessments, and audit trails.

For more information, see AI Automation Feature Guide.

|Available with Solution Packaging Only

Automatically scan cloud environments, APIs, pipelines, and applications to detect active AI models, agents, and datasets in your Databricks accounts. This feature captures metadata including model type, owners, input/output behavior, and associated datasets, providing a real-time, searchable inventory that serves as the foundation for governance policies, risk assessments, and audit trails.

For more information, see AI Automation Feature Guide.

|Available with Solution Packaging Only

Automatically scan cloud environments, APIs, pipelines, and applications to detect active AI models and agents in your Google Cloud accounts. This feature captures metadata including model type, owners, input/output behavior, and associated datasets, providing a real-time, searchable inventory that serves as the foundation for governance policies, risk assessments, and audit trails.

For more information, see AI Automation Feature Guide.

|Available with Solution Packaging Only

Automatically scan cloud environments, APIs, pipelines, and applications to detect active AI models in your SageMaker accounts. This feature captures metadata including model type, owners, input/output behavior, and associated datasets, providing a real-time, searchable inventory that serves as the foundation for governance policies, risk assessments, and audit trails.

For more information, see AI Automation Feature Guide.

|Available with Solution Packaging Only

Automatically scan cloud environments, APIs, pipelines, and applications to detect active AI models and agents in your Azure Foundry accounts. This feature captures metadata including model type, owners, input/output behavior, and associated datasets, providing a real-time, searchable inventory that serves as the foundation for governance policies, risk assessments, and audit trails.

For more information, see AI Automation Feature Guide.

Continuously scan, detect, and catalog 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.

For more information, see Automated AI Discovery: Amazon SageMaker Integration for AI Governance Models

Discover and catalog AI models, notebooks, and experiments across Google Cloud while maintaining centralized governance controls. 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.

For more information, see Automated AI Discovery: Google Vertex Integration for AI Governance Models.

|Available with Solution Packaging Only

Improve data use governance functions by automating dataset evaluation and surfacing metadata that clarifies data quality, sensitivity, and readiness. This accelerates access request reviews, ensures responsible data usage, and helps teams build AI models on compliant, trustworthy datasets, which enhances both accuracy and oversight.

Create a report of all records of a specific inventory and the columns eligible for bulk import, or the current saved view of an inventory screen including applied filters. This provides you with two flexible reporting methods to gather comprehensive insights into inventory records.

For more information, see Exporting AI Inventory Records.

OneTrust is planning to upgrade all environments to the new AI Governance Services, which provide improved permission granularity, expanded inventory support, foundations for agent governance, and a more unified management experience through the Inventory Manager. For more information and rollout schedules, seeAI Governance Services Upgrade Notice.

OneTrust is planning to unify the export output of AI Governance inventories to standardize values forSystem usersattributes. This change will replace the user's display name (e.g. John Smith) in favor of their email address (e.g. john.smith@onetrust.com) across all inventory types. Currently the platform has mixed behavior across the available inventory times, leading to import failures where the schema expects email address for these user fields.

Tenants will be automatically updated with this enhancement in the OneTrust 202604.2.0 release.

Issues that may impact our customers are documented in our Known Defects Database located here with approved workarounds and targeted releases for resolving the issue.