Table of Contents
- What Happened When Microsoft 365 Copilot Bypassed DLP Policies and Exposed Sensitive Business Information?
- Understanding the Microsoft Copilot Chat Security Incident
- Technical Details of the Vulnerability
- Microsoft’s Response and Remediation Timeline
- Broader Context: AI Security Challenges
- Risk Assessment for Enterprise Environments
- Preventive Measures and Best Practices
- Testing Concerns and Quality Assurance
What Happened When Microsoft 365 Copilot Bypassed DLP Policies and Exposed Sensitive Business Information?
Understanding the Microsoft Copilot Chat Security Incident
A security flaw in Microsoft 365 Copilot Chat allowed unauthorized access to confidential emails and file summaries, bypassing established data loss prevention (DLP) policies and sensitivity labels. Microsoft confirmed this issue through Advisory CW1226324 on February 3, 2026, acknowledging that emails marked with sensitivity labels were incorrectly processed by the chat function. The vulnerability specifically affected content stored in Sent Items and Drafts folders, grouping them together in chat responses on the Work tab despite configured protection measures.
Technical Details of the Vulnerability
The bug stemmed from a code error that caused Copilot Chat to ignore DLP policies designed to prevent processing of sensitive material. When users queried information through Copilot Chat, the system retrieved and displayed confidential content that should have remained restricted. This failure occurred even when organizations had implemented sensitivity labels and DLP configurations specifically to prevent such exposure. The incident demonstrates how AI systems with broad access permissions can become single points of failure when security controls malfunction.
Microsoft’s Response and Remediation Timeline
Microsoft began deploying a fix on February 11, 2026, approximately eight days after confirming the issue. The company simultaneously initiated contact with a subset of affected users to test the effectiveness of the patch. As of the current date, Microsoft has committed to providing a resolution timeline but has not yet completed this process, with the next scheduled update planned for February 18, 2026. This incident affects all users of Microsoft 365 Copilot who had the chat function enabled during the vulnerable period.
Broader Context: AI Security Challenges
This incident represents one of several documented vulnerabilities in Microsoft’s AI assistant tools. Earlier in 2025, researchers identified “EchoLeak” (CVE-2025-32711), a zero-click vulnerability that could have allowed attackers to exfiltrate sensitive data without any user interaction simply by sending specially crafted emails. Microsoft rated that vulnerability as critical and deployed a server-side fix in May 2025. These incidents highlight recurring concerns about prompt injection attacks and LLM scope violations, where untrusted input can compromise AI models’ access controls.
Risk Assessment for Enterprise Environments
Organizations deploying AI assistants with extensive access to corporate data face inherent risks when security controls fail. Copilot’s design grants access to user mailboxes, OneDrive storage, SharePoint sites, Teams conversations, and other Microsoft 365 resources. This broad permission scope means that any security flaw can potentially expose multiple data repositories simultaneously. The failure of DLP policies in this case raises questions about the reliability of traditional security controls when applied to AI-driven systems that process information differently than conventional applications.
Preventive Measures and Best Practices
While Microsoft handles server-side fixes for platform vulnerabilities, organizations should evaluate their AI deployment strategies. Consider implementing layered security approaches that don’t rely solely on DLP policies to protect sensitive information. Regular audits of which users have Copilot access and what data repositories the tool can query help minimize potential exposure. Organizations handling particularly sensitive information should assess whether the productivity benefits of AI assistants justify the security risks inherent in granting such broad data access to automated systems.
Testing Concerns and Quality Assurance
The incident raises legitimate questions about Microsoft’s testing procedures before releasing AI features. Testing whether DLP policies correctly prevent AI tools from accessing sensitivity-labeled content in common locations like Sent Items appears to be a fundamental validation step. When security controls designed to protect confidential data fail under standard operating conditions, it suggests gaps in pre-release quality assurance processes that should encompass realistic enterprise scenarios.