Microsoft Copilot Bug Bypasses Confidential Email Protections, Sparks Data Privacy Concerns

Copilot Bug Bypasses Confidential Email Protections, Sparks Data Privacy Concerns

A critical security flaw has been uncovered in Microsoft 365 Copilot Chat, revealing that the AI assistant can access and summarize emails explicitly labeled as confidential—even when Data Loss Prevention (DLP) policies are actively enforced to block such access. According to The Register, the bug allows Copilot to circumvent DLP rules designed to protect sensitive corporate communications, including those marked with sensitivity labels such as ‘Confidential’ or ‘Highly Confidential.’ This undermines core enterprise security assumptions and could expose proprietary business data, legal communications, and personally identifiable information to unintended AI processing.

The issue was first identified by enterprise IT administrators who noticed that Copilot Chat was generating summaries of restricted emails during routine user queries. Despite DLP policies configured through Microsoft Purview to prevent AI models from ingesting or processing labeled content, Copilot continued to parse the email bodies and extract key details. The behavior contradicts Microsoft’s public documentation, which states that Copilot respects sensitivity labels and DLP policies to ensure compliance with regulatory frameworks like GDPR and HIPAA.

While Microsoft has not issued an official public statement at the time of publication, internal support tickets and early user reports suggest the bug affects versions of Copilot integrated with Outlook on the web and Microsoft Teams. The flaw appears to stem from a logic gap in how Copilot interprets email metadata versus content classification. Rather than enforcing DLP rules at the data ingestion layer, the AI appears to rely on post-processing filters that can be bypassed if the email is opened or referenced in a chat context—even if the user lacks explicit permissions to view the content.

Security experts warn that this is not merely a technical glitch but a systemic risk. "This isn’t just about an AI reading an email—it’s about a trusted enterprise tool violating the very policies meant to protect data integrity," said Dr. Lena Torres, a cybersecurity researcher at the Center for Digital Trust. "If Copilot can ignore DLP labels, what’s stopping it from summarizing boardroom discussions, merger documents, or whistleblower reports? The trust model is broken."

Microsoft’s own product page for Copilot emphasizes its role as a "secure AI companion" designed to enhance productivity without compromising data governance. Yet the reality on the ground suggests a troubling disconnect between marketing claims and operational security. Organizations relying on Copilot for daily workflow automation may now be inadvertently exposing sensitive communications to third-party AI models, even when they believe they’ve taken all necessary precautions.

Industry analysts note that this is the latest in a growing pattern of AI safety oversights. Similar vulnerabilities have been reported in Google’s Gemini and OpenAI’s enterprise tools, where context-aware AI systems sometimes fail to respect access boundaries when integrated with productivity suites. The implications extend beyond compliance: reputational damage, regulatory fines, and legal liability could follow if confidential data is leaked via AI-generated summaries.

Microsoft has reportedly begun an internal investigation and is working on a patch. In the interim, enterprise customers are advised to disable Copilot Chat for users with access to sensitive email categories and to audit all DLP policy logs for anomalous AI activity. Until the flaw is resolved, the assumption that AI assistants respect data classification labels must be treated as untrustworthy.

As organizations accelerate adoption of generative AI tools, this incident underscores a sobering truth: AI integration must be governed by more than just feature lists—it requires rigorous, auditable security controls that are tested under real-world conditions. The era of AI as a silent, obedient assistant is over. In enterprise settings, AI must be treated as a high-risk actor—and secured accordingly.