Every organization wants to put AI to work on its data. Copilot, agents, and generative AI promise faster insight, but they raise a hard question for security teams: If AI can reach all our data, what stops it from surfacing the wrong data to the wrong person? The answer is to make your data AI-ready — classified, access-controlled, protected, and observable. Microsoft Fabric builds these capabilities directly into the platform through its integration with Microsoft Purview, so you can adopt AI with confidence instead of trading productivity for protection.
What “AI-ready” means for governance and security
“AI-ready” is often taken to mean accurate and well-structured. That’s only half the story. The moment you point a Copilot or agent at your data, AI inherits whatever access and protection gaps already exist — at machine speed and scale.
From a governance and security standpoint, AI-ready data is data where you can confidently answer four questions: What sensitive data do we have? Where does it live? Who can access it? How is it protected? The risk AI most amplifies is oversharing — an over-permissioned report or an unlabeled dataset that was a minor risk in a manual world becomes a major one when an agent can retrieve and summarize it on demand. In security terms, AI-ready means that sensitive data is discovered and classified; access is controlled and least-privilege; protection is persistent and portable; and activity on that data — human and AI — is observable and auditable.
Fabric data protection capabilities available today
Fabric brings Purview’s data security directly into the analytics experience, extending the same controls used across Microsoft 365 to your lakehouses, warehouses, semantic models, and reports.
- Sensitivity labels (Microsoft Purview Information Protection) — Classify Fabric items and Power BI content using the same labels you use across Microsoft 365 — manually, by default, or required via a mandatory label policy. The label and its protection travel with the data: when content is exported to Excel, PowerPoint, PDF, or .pbix, the encryption is enforced so only authorized users can open it. Downstream inheritance and APIs drive coverage at scale.
- Protection policies — Each policy is tied to a sensitivity label and restricts access to labeled items — named users and groups keep access, everyone else is blocked. Supported for native Fabric items and semantic models.
- DLP policies — Defined in Purview and scoped to Fabric and Power BI workspaces, DLP automatically detects sensitive info types and labels in semantic models and OneLake structured items (like lakehouses, SQL DB, Mirrored databases, and KQL databases, etc.), then triggers policy tips, security admin alerts, and access restrictions with DLP restrict access. You can run the policies in simulation mode first to tune the rules.
- DLP restrict access (Preview) — The enforcement action that turns detection into protection — automatically restricting who can reach a matched item, including blocking external users from accessing sensitive internal data or in more sensitive cases restrict access to admins only.
- Protection insights — The Protect, secure, and comply report in OneLake Catalog (Govern) shows tenant-wide label coverage, while audit logs track label activity and DLP detections.
Figure: Protect, secure and comply report in Microsoft Fabric OneLake Catalog Govern tab.
- DSPM for Fabric — Microsoft Purview Data Security Posture Management helps discover data risks — such as sensitive data in user prompts and responses — and provides recommended actions to reduce these risks. It also enables Data Risk Assessments that identify potentially overshared, unprotected, or sensitive Fabric assets, giving teams clear visibility into data exposure and enabling targeted risk-reduction actions, such as applying labels or policies.
How they make your data AI-ready
These capabilities map directly to the AI-readiness questions previously mentioned:
- Labels and DLP answer what do we have and where by classifying content and detecting sensitive data even where it’s unlabeled.
- Security insights in the OneLake Catalog and DSPM make it all observable, showing where the sensitive data is.
- Protection policies and DLP restrict-access protect who can access it — and because AI operates within a user’s permissions, tightening access directly constrains what Copilot and agents can return.
- Label inheritance keeps protection on data even when exported to Office files, closing one of the more common leak points.
- And DSPM makes AI usage observable, giving teams evidence of what AI is actually doing with sensitive data — and the chance to contain oversharing before it’s amplified.
Together, these capabilities let you federate data broadly for AI without compromising protection.
Figure: Fabric data protection capability answers one or more of the four AI-readiness requirments.
Next steps
- Enable and publish sensitivity labels, then drive coverage with default and mandatory labeling
- Add protection policies to your most sensitive labels for hard access control.
- Create DLP policies — start in simulation, then enable policy tips, alerts, and restrict access.
- Establish visibility via the data protection report, audit logs, and Defender for Cloud Apps.
- Get AI-ready with DSPM — activate one-click policies and the Fabric data risk assessment.
AI-ready data is well governed data. Because these protections are built into Fabric, you can classify, control, protect, and observe in one place — and adopt AI across your estate with confidence.
Learn more
Power BI Team