Introduction
Power Platform offers multiple AI capabilities, ranging from Copilot Studio agents to AI Builder models to generative prompts. Organizations can use these capabilities to improve their business processes. But without deliberate governance, those same capabilities create risks that you must mitigate. These risks include sensitive data flowing to uncontrolled AI endpoints, models trained on inappropriate content, and agents answering questions with unverified information.
Scenario: Uncontrolled AI adoption at Zava
Zava's marketing team built a Copilot Studio agent that answers customer questions using public website knowledge sources. Meanwhile, Zava Pay's compliance team created AI Builder models to detect fraudulent transactions. A store manager used AI prompts to generate employee communications. None of these efforts were coordinated and none were reviewed for data residency compliance, responsible AI principles, or appropriate knowledge source boundaries.
The Zava Pay agent inadvertently uses web search grounding that sends financial context to Bing endpoints. The marketing agent answers questions using unverified web content with no human oversight. Custom models sit in environments without customer-managed encryption. The admin team needs a framework that governs AI-enabled resources specifically - one that layers on top of their environment strategy and data policies.
What you learn in this module
This module builds on your environment topology and data policy foundations to address AI-specific governance:
- Establishing responsible AI principles and content moderation across the organization.
- Controlling data residency and cross-region movement for AI processing.
- Managing which knowledge sources and AI capabilities are available to makers.
- Administering custom AI models, prompts, and semantic search features.
By the end of this module, you can configure AI governance settings that enforce responsible and compliant AI use across your Power Platform environments.