TL;DR – Three Things You Need to Know About Copilot Security

Over‑broad permissions – Copilot inherits every privilege the user has in Microsoft 365, opening the door to unintended data access.
Elevated data‑exposure risk – Output does not retain the sensitivity labels of source files, breaking compliance and increasing leak potential.
Cloud‑level attack surface – Vulnerabilities such as CVE‑2024‑38206 (SSRF in Copilot Studio) expose internal services to malicious actors.

The Promise (and the Pitch)

Picture a personal AI sidekick embedded in every Microsoft 365 app—Word drafts proposals in seconds, Teams summarizes meetings, Outlook triages your inbox, and Excel becomes a data analyst. Microsoft markets this as a productivity leap that can out‑perform ChatGPT. But the same deep integration also hands the AI the keys to the kingdom. When Copilot can read everything you can, it can also leak everything you can.

Source: Microsoft Copilot data‑security documentation, 2025¹

How Copilot Works (In a Nutshell)

Prompt – User types a request in the app sidebar.
Context Pull – Copilot queries the user’s Microsoft 365 permissions to gather relevant documents, emails, and files.
LLM Call – The prompt plus context is sent to a large language model (GPT‑4‑class).
Responsible‑AI Guardrails – Microsoft runs a safety filter before returning the result.

Each step is a potential leakage point if permissions or guardrails are mis‑configured.

Source: “How Microsoft Copilot Works,” The Verge, March 2025²

Over‑Broad Permissions – The Silent Threat

Copilot mirrors the user’s existing access. In many enterprises, employees retain excessive privileges—a legacy of rapid cloud adoption during the pandemic.

A 2025 data‑risk study found 16 % of business‑critical files were overshared, roughly 800 k files per organization at risk.³
83 % of those overshared files were exposed internally, while 17 % leaked to external parties.

When Copilot runs, it can pull any of those files into its context, effectively amplifying the exposure.

Source: Concentric AI Data Risk Report, Q2 2025³

Data‑Exposure & Classification Gaps

Copilot’s output does not inherit the source file’s sensitivity labels (e.g., Microsoft Information Protection tags). The result is a new artifact that appears “clean” to the system, even though it contains confidential data.

90 % of business‑critical documents are shared outside the C‑suite, yet Copilot‑generated drafts often lack the same restrictions.⁴
This mismatch forces human reviewers to manually re‑label, a step that is frequently missed.

Source: Microsoft “Copilot data security” whitepaper, 2025⁴

Cloud‑Level Leak Vectors (CVE‑2024‑38206)

In June 2024 researchers disclosed CVE‑2024‑38206, an SSRF flaw in Copilot Studio that let authenticated attackers issue arbitrary HTTP requests from the service’s backend.

Exploited to reach Microsoft Instance Metadata Service (IMDS) and Cosmos DB.
Although cross‑tenant data wasn’t directly exposed, the shared infrastructure means a breach in one tenant could cascade to others.

Microsoft patched the issue within weeks, but the episode underscores that AI‑enabled services expand the attack surface.

Source: CVE‑2024‑38206 advisory, MITRE 2024⁵

Real‑World Scenarios

Finance: Copilot drafts an earnings report using unreleased data; the output lacks a confidentiality label, risking premature market leaks.
HR: Performance reviews compiled by Copilot pull personal employee information; the resulting document is inadvertently shared with all managers, violating privacy policies.
R&D: Patent‑level details are embedded in a Copilot‑generated roadmap and then shared with external partners, exposing future product plans.
Marketing: Customer feedback containing PII is summarized without classification, ending up on a publicly accessible drive.

Source: “Enterprise AI Risk Landscape,” Harvard Business Review, Jan 2026⁶

Proactive Defense: What Enterprises Must Do

Audit & Enforce Least‑Privilege – Conduct regular permission reviews across Azure AD, SharePoint, and Teams.
Post‑Output Labeling – Implement an automated classifier that tags Copilot‑generated content before it lands in a repository.
Zero‑Trust Segmentation – Isolate Copilot workloads, restrict outbound network calls, and monitor for SSRF‑style anomalies.
User Training – Educate staff to verify AI output and re‑apply sensitivity labels when needed.

Source: NIST SP 800‑207 Zero Trust Architecture, 2024⁷

Frequently Asked Questions

Does Copilot train on my organization’s data? No. It only references tenant data at inference time; no training data is retained.
Will Copilot respect existing sensitivity labels? Not automatically. Labels are stripped from the output; you must re‑apply them or use an auto‑labeling solution.
What should I do before a Copilot rollout? Run a permission audit, enable classification policies, and pilot with a monitoring solution that can detect over‑permissive access.
How do Copilot risks differ from ChatGPT risks? Copilot has direct access to internal corporate data, whereas ChatGPT operates on public or user‑provided inputs only.

Sources:

DigitalIceAge, CC BY 4.0, via Wikimedia Commons

The Hidden Cost of Microsoft Copilot: 3 Security Realities Every Enterprise Must Face