AI Governance - June 5, 2026 - 202606.1.0

General Availability

Last Completed Automation Rule for AI Governance Inventories

Configure automation rules to take actions using the new Last Assessment Complete Date - All Templates and Last Assessment Completion Date - By Template conditions. This enhancement enables governance teams to maintain up-to-date inventories and reassess them as needed by triggering automation based on the last assessment completed date for any completed assessment across all templates or a specific template.

For more information, see Configuring Automation Rules for AI Governance. This feature is related to the following idea: AI-I-81.

Fixed

• Resolved an issue that attached file extensions (e.g., .ptt, .doc) to the name of documents added to Project inventories and prevented them being opened locally as expected.

New Public Preview Features

AI Guard SDK and Classification

Protect critical AI systems by introducing AI Guard SDK by OneTrust, a comprehensive classification and inspection layer that helps ensure AI interactions are safe, secure, and compliant during development and runtime. This enhancement helps identify PII risks early through advanced data classification, enabling teams to test AI agents and quantify PII risk before moving to production. Integrate easily using the SDK-based approach designed for Python runtime environments, ideal for build-phase validation rather than high-volume, externally facing AI use cases.

For more information, see OneTrust AI Guard SDK Guide.

Observability for Amazon Bedrock

Extend continuous AI monitoring for Amazon Bedrock to gain visibility into AI agent and model activity. This enhancement ongoing governance by capturing agent and model metadata, usage information, evaluation metrics and more directly from Amazon Bedrock powered AI systems as they operate in production.

Additionally, you can detect and measure PII leakage for Amazon Bedrock using the AI Log Analyzer by scanning AI logs stored in Amazon S3, identifying sensitive prompts and responses, and surfacing PII counts in OneTrust for the associated AI assets to reduce risk from non-deterministic AI behavior.

For more information, see the following:

• Observability Usage of AI Governance Agents & Models

• AI Governance Model Evaluation Metrics

• AI Policy Enforcement Guide

• OneTrust AI Log Analyzer

Observability for Microsoft Foundry

Enable continuous monitoring for AI agents and models built on Microsoft Foundry by surfacing Evaluation Metrics for Agents and Models shifting from AI systems. This enhancement helps shift from one-time assessments to ongoing evaluation using live telemetry from Foundry-based AI systems.

For more information, see the following:

• AI Governance Model Evaluation Metrics

• AI Policy Enforcement Guide

Existing Public Preview Features

AI Policy Manager & Seeded AI Policies

Automatically detect AI policy violations across platforms to provide AI governance teams with actionable steps to ensure governance and compliance throughout an AI Agent lifecycle. This policy orchestration experience allows users to create custom policies for unique scenarios and support guardrail enforcement and detection.

To compliment AI policy management, seeded AI policies are also available to flag and provide enforceable steps on guardrails. This reduces the time to value for customers getting started with AI Policies and provides a template for them to use when defining their own custom AI Policies in OneTrust.

For more information, see AI Policies Feature Guide.

Guardrail Detection & Enforcement

Automatically enforce responsible AI policies on violating agents and models across AI development platforms. This enhancement to AI policies provides consistent guardrail visibility and enforcement measures in the OneTrust Platform.

For more information, see AI Policy Enforcement Guide.

Custom Notifications for Assessment Templates

Send customized, trigger-based automated email messages from a new global Notifications tab on the Assessment templates screen. This enhancement speeds up assessment completion, reduces support requirements, and provides users with more relevant information in the assessment.

For more information, see Configuring Custom Email Notifications on Assessment Templates. This feature is related to the following ideas: OT-I-949, OT-I-1270, OT-I-3522, OT-I-7945, and OT-I-16348.

Configuring Approver-only Assessment Sections

Configure approver-only sections on assessments automatically using template rules or inventory-based values. These enhancements mean users can launch assessments with approver-only sections directly from the self-service portal.

For more information, see Configuring Approver-only Assessment Sections. This feature is related to the following ideas: AA-I-293 and AA-I-302.

Leveraging AI Auto-responses for Assessments

Opt-out of automatically scanning your three most recent assessments, plus add more files, more file types, and bigger files to your uploads. These enhancements improve the performance and accuracy of the AI Doc Scanner.

For more information, see Leveraging AI Auto-responses for Assessments.

Automated AI Discovery

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

Automated AI Discovery

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.

Metadata for Datasets

|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.

Export Inventory Records to Column Report

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.

Things to Know

About Known Defects

 

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.