Securing AI in Microsoft Purview: Data Governance for Generative AI – Classification, Labeling, and Risk Assessment for AI-Generated Content
Microsoft Purview provides a robust framework for data governance, classification, labeling, and risk assessment to secure these interactions while enabling safe innovation.
Generative AI tools like Microsoft 365 Copilot, Copilot Studio, Azure OpenAI, and third-party LLMs are transforming productivity. Yet they introduce new risks: sensitive data in prompts, AI-generated outputs that may leak information, and amplified oversharing due to AI’s speed and reach. Microsoft Purview provides a robust framework for data governance, classification, labeling, and risk assessment to secure these interactions while enabling safe innovation.
Why Data Governance Matters for Generative AI
AI doesn’t create entirely new risks – it accelerates existing ones. Prompts can inadvertently include PII, IP, or regulated data. Outputs may inherit or expose sensitivities. Without governance, “security by obscurity” fails as AI surfaces content across broad access.
Purview addresses this through Data Security Posture Management for AI (DSPM for AI), information protection, DLP, and more. It offers visibility, automated controls, and compliance across Copilot experiences, Entra-registered AI apps, and browser-based generative AI.
1. Data Discovery and Classification for AI Interactions
Start with visibility. Purview scans your data estate (SharePoint, OneDrive, Azure, etc.) using:
Built-in sensitive information types (300+ out-of-the-box for PII, financial data, health records, etc.).
Trainable classifiers and custom classifiers tuned for your domain.
AI-powered classification that handles content from or for LLMs, including synthetic data testing.
These extend to AI prompts and responses. Sensitive data in user inputs or AI outputs surfaces in Activity Explorer (AI activities tab), DSPM reports, and unified audit logs.
Practical steps:
Enable auto-classification policies in the Purview portal.
Use DSPM for AI data assessments (preview/GA) to identify oversharing risks in SharePoint sites and AI-accessible content.
Review recommendations for one-click remediations like labeling high-risk sites.
This inventory helps prioritize risks before AI adoption scales.
2. Sensitivity Labeling: Protection That Travels with AI-Generated Content
Sensitivity labels are central. They apply markings, encryption, and access controls that AI respects.
Key behaviors:
Inheritance: Copilot in Word, PowerPoint, Outlook, and Chat automatically applies the highest-priority label from source files to new AI-generated content. Protections (encryption, watermarks, footers) carry over.
Enforcement in AI: Labels block unauthorized access in reasoning. Users need VIEW + EXTRACT rights for encrypted content. Copilot surfaces label info in responses and citations.
Auto-labeling: Extend policies to AI interactions for consistent application.
Implementation tips:
Enable sensitivity labels for SharePoint/OneDrive.
Define label priorities (higher number = more restrictive).
Use auto-apply policies based on classifiers or content inspection.
Labels ensure AI-generated documents, summaries, or chats remain protected even when shared or stored.
3. Risk Assessment and Posture Management with DSPM for AI
DSPM for AI acts as your “front door” for AI governance:
Visibility into interactions: Prompts, responses, sensitive data shared, and risky behaviors (e.g., jailbreaks, unethical use).
Data risk assessments: Weekly scans for oversharing; custom assessments for Copilot-specific risks.
Personalized recommendations: One-click policies for labels, DLP, or Insider Risk.
Reporting: Insights flow to Activity Explorer, with drill-down to users and apps (Microsoft 365 Copilot, ChatGPT Enterprise, etc.).
Combine with Compliance Manager for regulatory mapping (e.g., GDPR, HIPAA) and guided AI controls.
4. Preventing Leaks: DLP, Insider Risk, and More
Data Loss Prevention (DLP): Block or warn on pasting sensitive content into AI sites via Endpoint DLP. Specific policies for Copilot locations restrict prompts/files with certain labels or SITs.
Insider Risk Management: Detect risky AI usage (prompt injection, protected material access) with templates. Adaptive Protection dynamically tightens DLP for high-risk users.
Communication Compliance: Monitor prompts/responses for policy violations (harassment, sensitive sharing).
Auditing & eDiscovery: Full logs of AI activities, including referenced files and labels. Search and preserve AI-generated content.
Best Practices for Implementation
Start small: Use out-of-the-box SITs and DSPM assessments for quick wins.
Governance first: Classify and label core data estates before broad AI rollout.
User education: Labels provide in-context awareness; train on responsible prompting.
Monitor and iterate: Review DSPM dashboards regularly; refine classifiers and policies.
Extend to custom AI: Use Purview with Azure AI, Foundry, and Entra-registered apps.
For organizations like those running Security MVP programs or building AI agents, Purview integrates seamlessly with Defender XDR and Sentinel for unified risk views.
Conclusion: Govern AI with Confidence
Microsoft Purview turns generative AI risks into governed opportunities. Through classification, persistent labeling, DSPM-driven assessments, and layered protections, you secure data in prompts, processing, and outputs – all while maintaining compliance and productivity.
As AI evolves, proactive data governance is non-negotiable. Explore DSPM for AI in the Microsoft Purview portal today, and check the latest in Learn docs for your specific Copilot and AI scenarios.
What are your biggest AI governance challenges? Share in the comments or connect on LinkedIn/X (@rodtrent). For more on Purview, Defender, and AI agents, subscribe or check my Substack.