Before you deploy AI, your existing data environment becomes the foundation for everything AI will touch. If that foundation isn’t secure, AI can unintentionally expose or amplify risks you already have.

Organizations are already feeling this pressure:

• 83% of organizations experience more than one data breach in their lifetimes.
• 30% of decision-makers say they lack visibility into all their business-critical data.
• Data leakage is cited as the top concern by many leaders considering AI.

As employees adopt AI, often without going through official channels, several risks grow quickly:

• Data oversharing – AI apps may surface sensitive files to users who shouldn’t see them if access controls and labels are weak.
• Data leakage – Staff may paste confidential information into unsanctioned AI tools, sending sensitive data outside your control.
• Non-compliant usage – AI can be used to generate content that violates ethics standards or regulations (for example, content that hides insider trading or other illegal activity).

Preparing your data before AI deployment helps you:

• Understand where sensitive data lives and who can access it.
• Clean up old, obsolete, or overshared content and permissions.
• Apply labels and protection so AI-generated outputs inherit the right controls.
• Put data loss prevention policies in place to stop accidental exfiltration.

This upfront work lets you adopt AI with more confidence, reduce regulatory exposure, and support innovation without putting sensitive data at unnecessary risk.

You can think of AI readiness as a structured data hygiene program. The guidance in the source material breaks it into four practical steps:

1. Know your data
   Use tools like Microsoft Purview Information Protection to gain visibility into sensitive information:
   • Use Content Explorer and Activity Explorer to locate sensitive data and see how it’s being used.
   • Classify and label sensitive data with built-in or custom Sensitive Information Types (SITs).
   • Allow users to apply labels directly in Microsoft 365 apps as they work.

   This helps you understand what data AI might reference and where you’re exposed.

2. Clean up and govern your data
   Reduce risk and support compliance by tightening access and lifecycle management:
   • Review SharePoint sites and file permissions; identify overshared content.
   • Apply SharePoint-wide policies for content management.
   • Delete old or obsolete data that no longer needs to be discoverable by AI.
   • Use Purview machine learning classifiers to detect and mitigate risky content.

   This step helps you avoid non-compliant AI usage and potential fines later.

3. Protect your data
   Put protection directly on the data so AI respects it:
   • Use Purview Information Protection to classify, label, and protect content based on sensitivity.
   • Configure labels to apply encryption, rights management, and watermarks where needed.
   • Set clear label policies (for example, “General” can be broadly shared; “Highly Confidential” is encrypted and restricted).

   With Copilot, any output that references labeled content inherits the highest sensitivity label the user is allowed to see, extending protection to AI-generated responses.

4. Prevent data loss
   Use Microsoft Purview Data Loss Prevention (DLP) to stop sensitive data from leaving approved channels:
   • Configure DLP policies to monitor and block risky actions such as uploading to cloud services, copying to USB, or external sharing.
   • Extend DLP to Windows 10 devices, the Chrome browser, on-premises file shares, SharePoint libraries, and Teams chats and channels.
   • Apply DLP controls to AI prompts so users can’t paste sensitive data into unsanctioned AI tools.

   This helps protect both existing and AI-generated data from accidental or intentional exfiltration.

By following these four steps before deployment, you create a data environment where AI can operate effectively while respecting your security, privacy, and compliance requirements.

Copilot for Microsoft 365 is designed to fit into your existing Microsoft 365 environment, which helps you reimagine work with AI while staying aligned with your current security and compliance posture.

Several aspects stand out for security-conscious organizations:

• Built on your existing controls
  Copilot runs on top of Microsoft 365 and uses the same privacy, security, and compliance commitments you already rely on. It only accesses content the user is already authorized to see.
• Data protection by design
  Your data:
  • Is encrypted.
  • Is not used to train foundational large language models in Copilot.
  • Respects your data location and residency choices, including EU data boundary options.
  • Benefits from commercial data protection for web-grounded prompts.
• Label-aware AI behavior
  When you use Microsoft Purview Information Protection:
  • Copilot inherits sensitivity labels from referenced files.
  • AI-generated outputs automatically take on the highest sensitivity level of the content used, extending protections like encryption and watermarking.
• Governance and compliance tooling
  With Microsoft Purview AI Hub and related capabilities, you can:
  • Discover how Copilot and other AI apps are being used, including total AI interactions and associated risk levels.
  • Identify sensitive data flowing through AI prompts and responses, including unlabeled files referenced by Copilot.
  • Use Audit to log Copilot interactions, Data Lifecycle Management to retain or delete interaction content, Communication Compliance to detect non-compliant prompts, and eDiscovery to search interactions for investigations.
• Support for secure adoption paths
  Copilot is available as an add-on for Office 365 E3/E5 and Microsoft 365 E3/E5. An online optimization assessment helps you choose between a Core or Best-in-class deployment path, depending on how advanced you want your security and compliance controls to be.

In a landscape where 75% of knowledge workers are already using AI at work and 78% of AI users are bringing their own tools, Copilot offers a way to bring that activity into a managed, policy-driven environment. That lets you support productivity gains while keeping data protection and regulatory obligations front and center.