Taming Data Oversharing Before Copilot

Introduction

As organizations increasingly leverage the power of Artificial Intelligence, particularly with the advent of tools like Microsoft Copilot, the conversation around data security and governance has intensified. Copilot, designed to enhance productivity by working with an organization's data, inherently raises questions about information access and potential oversharing. Before fully embracing these AI capabilities, a critical prerequisite is to establish robust data governance, specifically focusing on mitigating unintended data oversharing.

This article delves into the proactive strategies and Microsoft 365 capabilities necessary to ensure your sensitive data is appropriately protected and accessible only to authorized personnel. We will explore how to identify, classify, and control access to information, creating a secure foundation that prevents Copilot (and by extension, your employees) from inadvertently exposing sensitive details. This guide is tailored for IT professionals, security administrators, and compliance officers responsible for safeguarding organizational data within the Microsoft 365 ecosystem.

Why this matters

The impulse to address data oversharing before deploying AI tools like Copilot is not merely a best practice; it's a fundamental requirement for maintaining security, compliance, and operational integrity. Uncontrolled data access can lead to significant risks:

• Compliance Violations: Regulations such as GDPR, HIPAA, and CCPA impose strict requirements on how personal and sensitive data is handled. Oversharing can result in hefty fines and reputational damage.
• Data Breaches: Inadvertent exposure of sensitive intellectual property, financial records, or customer data can lead to catastrophic data breaches, impacting customer trust and business operations.
• Reduced Productivity & Increased Risk: When AI tools can access an ocean of undifferentiated data, the risk of them surfacing inappropriate or irrelevant information increases. This can lead to inefficient use of AI, and even worse, expose the organization to legal or ethical liabilities if AI acts on overshared data.
• Shadow IT & "Bring Your Own AI": Without formal controls, users might seek alternative, less secure AI solutions, further exacerbating data oversharing risks outside of approved organizational boundaries.
• Cost Management: While not immediately obvious, remediation efforts following a data oversharing incident can be exorbitantly expensive, including forensic investigations, legal fees, and communication to affected parties. Proactive measures are far more cost-effective.

Key concepts

To effectively combat data oversharing in the Microsoft 365 landscape, understanding these core concepts and services is crucial:

• Microsoft Purview Information Protection (MPIP): A suite of capabilities within Microsoft Purview that helps you discover, classify, label, and protect sensitive information across your digital estate.
• Sensitivity Labels: Persistent, customizable tags that you can apply to documents and emails. These labels can enforce protective actions such as encryption, visual markings, and access restrictions, ensuring protection travels with the data.
• Data Loss Prevention (DLP) Policies: Rules configured to detect, warn, or block users from sharing sensitive information inappropriately, both internally and externally. DLP policies rely on sensitive information types (SITs) to identify content.
• Sensitive Information Types (SITs): Patterns, keywords, or classifications that allow Purview to identify specific types of sensitive data, like credit card numbers, national IDs, or healthcare data. Purview includes many built-in SITs, and you can create custom ones.
• Adaptive Protection: A Purview feature that dynamically adjusts DLP policy enforcement based on a user's risk profile, utilizing insights from Microsoft Purview Insider Risk Management.
• Permissions and Access Controls: The fundamental mechanism for regulating who can access what. In Microsoft 365, this includes SharePoint site permissions, OneDrive sharing settings, Teams channel access, and Azure Active Directory (now Microsoft Entra ID) group memberships.
• Information Barriers: A compliance solution in Microsoft 365 that prevents communication and collaboration between specified groups of users, crucial for preventing conflicts of interest and protecting sensitive internal data.

Step-by-step implementation

Implementing robust data oversharing controls involves a systematic approach:

1. Discover and Classify Sensitive Data:

Initiate a data discovery process using Purview Content Explorer and Activity Explorer. Action: In the Microsoft Purview portal, navigate to Information Protection -> Data Classification -> Content explorer to identify current locations of sensitive data. Action: Create and publish Sensitivity Labels via Information Protection -> Labels*. Define labels for different levels of sensitivity (e.g., "General," "Confidential," "Highly Confidential"). Configure automatic labeling policies to apply labels based on content matching SITs. ```powershell # Connect to Exchange Online PowerShell Connect-IPPSSession

# Create a new sensitivity label (example) New-Label -Name "Highly Confidential" -DisplayName "Highly Confidential" -Color "#FF0000" -Tooltip "This information is highly confidential." -Priority 100 -Comment "Label for highly confidential data."

# Publish the label policy (example) New-LabelPolicy -Name "Global Sensitivity Labels" -Labels @("Highly Confidential", "Confidential", "General") -DefaultLabel "General" -BlockOutlookWebAccess -BlockTeamsChats -AllowUserOverride False

# Apply encryption and access permissions to a label (requires Azure Information Protection P2) Set-Label -Identity "Highly Confidential" -EncryptionEnabled $true -EncryptionRights "admin@yourdomain.com", "financegroup@yourdomain.com:View" -EncryptionKeyLocation Azure ```

1. Implement Data Loss Prevention (DLP) Policies:

Action: In the Microsoft Purview portal, go to Data Loss Prevention -> Policies. Create new policies using built-in templates (e.g., "Financial," "Medical and Health") or custom policies. Configuration: Target specific locations (Exchange email, SharePoint sites, OneDrive accounts, Teams chats and channel messages, Microsoft Entra ID identities, and Endpoint devices). Rules:* Configure rules to detect specific sensitive information types (SITs) and apply actions like blocking sharing, sending notifications, or prompting users with policy tips. Start with audit mode or "Block with override" to minimize disruption.

1. Refine Permissions and External Sharing Settings:

Action: Regularly review SharePoint site permissions. Utilize Microsoft 365 Groups for access management, ensuring group membership is up-to-date. Action: Configure default external sharing settings for SharePoint and OneDrive in the SharePoint admin center -> Policies -> Sharing. Restrict external sharing to "Existing Guests" or "Specific people" where appropriate. Action: Implement Information Barriers for segments of your organization that deal with highly sensitive data or internal regulatory requirements (e.g., legal counsel, M&A teams). Configure these in the Microsoft Purview portal -> Information Barriers*.

1. Monitor and Audit Data Access:

Action: Utilize the Microsoft 365 Audit Log to track file access, sharing events, and changes to permissions. Access this via the Microsoft Purview portal -> Audit. Action: Use Microsoft Defender for Cloud Apps (MDCAS) policies to monitor sensitive file activities, detect unusual sharing patterns, and enforce real-time controls. Configure policies such as "Sensitive files shared externally" or "Activity from infrequent country."

1. Educate Users:

Conduct regular training sessions on data classification, sensitivity labels, and the importance of data governance. Emphasize the risks associated with oversharing and the role each employee plays in maintaining data security.

Example configuration

Here's an example of a JSON snippet representing a simplified Purview Sensitive Information Type definition for a custom identifier, which could then be used in a DLP policy or Sensitivity Label auto-labeling. This example defines a "Project Alpha ID" that follows a specific pattern.

{
  "$schema": "http://microsoft.com/sit/schemas/2021-08-30/custom.sit.schema.json",
  "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "name": "Project Alpha ID",
  "description": "Custom SIT for Project Alpha internal identifiers",
  "publisherName": "Zunair Tech",
  "version": "1.0",
  "regexes": [
    {
      "pattern": "PROJECTALPHA-(?:[A-Z]{3}-\\d{4})",
      "caseSensitive": false,
      "minOccurrences": 1,
      "maxOccurrences": 10,
      "confidence": "high",
      "recommendedConfidence": 85
    }
  ],
  "keywords": [
    {
      "word": "project alpha id"
    },
    {
      "word": "alpha identifier"
    }
  ],
  "proximity": 300,
  "supportedLanguages": [
    "en"
  ]
}

Common pitfalls

1. "Big Bang" DLP Deployment: Deploying all DLP policies simultaneously in enforcement mode without prior testing or an audit period can lead to widespread user disruption and frustration, hindering adoption.
2. Over-reliance on Manual Labeling: Expecting users to manually apply sensitivity labels consistently across all content is unrealistic and often results in significant data remaining unclassified and unprotected.
3. Ignoring External Sharing Risks: Focusing solely on internal data flow while neglecting the often-broader risks associated with external sharing, guest accounts, and unmanaged devices.
4. Poorly Defined Sensitive Information Types: Using overly broad or too narrow SITs can lead to excessive false positives (blocking legitimate content) or false negatives (failing to detect sensitive data).
5. Lack of User Education: Implementing technical controls without accompanying user training and communication fosters resistance and circumvention, undermining the entire security posture.
6. Neglecting Cross-Tenant Data: If your organization collaborates with other tenants, failing to manage and restrict data flows across these tenant boundaries.

Best practices

• Adopt a "Discover, Audit, Enforce" Cadence: Start by discovering where your sensitive data resides. Implement policies in audit mode first to understand their impact, then gradually move to enforcement. This aligns with the Microsoft Cloud Adoption Framework's guidance on iterative implementation.
• Automate Classification via Sensitivity Labels: Leverage automatic and recommended labeling policies within Microsoft Purview to ensure consistent and scalable data classification, reducing reliance on manual user intervention.
• Implement Principle of Least Privilege (Zero Trust): Grant users only the minimum access necessary to perform their job functions. Regularly review and revoke unnecessary permissions. This is a cornerstone of the Zero Trust security model.
• Combine DLP with Information Barriers: For highly regulated industries or departments, complement DLP policies with Information Barriers to enforce stricter communication boundaries between segmented groups.
• Regularly Review and Refine Policies: Data landscapes evolve. Periodically review your SITs, DLP policies, and sensitivity labels to ensure they remain effective and aligned with current business needs and compliance requirements.
• Integrate with Insider Risk Management: Utilize insights from Microsoft Purview Insider Risk Management to automatically adjust DLP policy enforcement based on a user's risk profile, thereby introducing adaptive protection.

Further reading

• Learn about sensitivity labels - Microsoft Purview
• Learn about data loss prevention - Microsoft Purview
• Plan for information barriers - Microsoft Purview
• Create custom sensitive information types - Microsoft Purview
• What is Microsoft Copilot?