A practical guide for IT admins, security teams, and M365 administrators who need to govern Copilot before it surfaces data it should not.
Reading time: 9 minutes
Microsoft 365 Copilot is one of the most powerful productivity tools ever embedded into the enterprise workspace. It drafts emails, summarises meetings, generates reports, analyses spreadsheets, and searches across your entire Microsoft 365 tenant to find the answers your people need, fast.
But here is the reality that IT and security teams are learning the hard way: Copilot does not create new access. It exposes existing access. And for most organisations, that existing access is far messier than anyone thought.
Research from Concentric AI found that 16% of business-critical data in the average organisation is overshared, with over 800,000 files at risk due to broad permissions. A separate study found that 67% of enterprise security teams report concerns about AI tools potentially exposing sensitive information. And the issue is not theoretical: the U.S. House of Representatives banned congressional staff from using Copilot specifically over data security concerns.
The good news is that every one of these Copilot security risks is solvable with the right governance framework and Microsoft-native controls. This post walks through the five most dangerous risks and exactly how to fix each one.
A marketing executive asks Copilot: "Summarise the latest financial performance data." Copilot searches across Microsoft 365 and returns a summary that includes revenue figures, margin analysis, and a salary comparison table from an HR spreadsheet. The marketing executive should never have seen this data, but somewhere in the past, an old SharePoint site was shared with "Everyone except external users" and nobody ever revoked that permission.
This is the most common and most dangerous Copilot security risk. Copilot operates within a user's existing Microsoft 365 data boundary, which means it can access any content that user already has permission to see. When permissions are overly broad, inherited from old configurations, or set to organisation-wide by default, Copilot will surface that data in its responses.
Concentric AI's Data Risk Report found that over 3% of business-sensitive data was shared organisation-wide without any consideration for whether it should have been. At enterprise scale, that translates to hundreds of thousands of files exposed to AI-powered retrieval.
Copilot data governance starts with permission hygiene. Before enabling Copilot across your tenant, you need to:
This is not a one-time cleanup. It requires an ongoing governance process that reviews permissions quarterly, monitors new sharing activity, and enforces least-privilege access as a standard operating procedure.
A well-intentioned operations manager builds a custom Copilot agent in Copilot Studio that connects to an internal customer database via a Power Platform connector. The agent is shared with the entire sales team to speed up customer lookups. The problem: the agent has no authentication requirements, no DLP policy applied, and nobody in IT or security was consulted. The agent is pulling customer PII into chat responses that are not logged, not labelled, and not governed.
Shadow AI is not limited to employees using ChatGPT on personal accounts. Inside the Microsoft ecosystem, it now includes Copilot Studio agents, Power Automate flows with AI Builder actions, and custom agents built in Teams. Microsoft's own security blog recently detailed ten common agent misconfigurations observed in production environments, including agents that operate without authentication, send emails with dynamic inputs controlled by external parties, and make HTTP requests that bypass connector governance entirely.
Governing Copilot agents requires a layered approach across the Power Platform and M365 admin centres:
The goal is not to block agent creation. It is to ensure every agent operates within governed boundaries with proper authentication, data controls, and visibility.
A threat actor embeds hidden instructions inside a document or email that ends up in your SharePoint library. When a user asks Copilot to summarise that content, the embedded instructions manipulate Copilot's behaviour, causing it to search for additional sensitive data, generate misleading responses, or exfiltrate information piece by piece through follow-up requests.
This is not hypothetical. Security researchers at Varonis discovered a vulnerability called "Reprompt" that allowed attackers to hijack Copilot sessions through malicious links. The attack worked by establishing a persistent back-and-forth where Copilot kept receiving instructions from a remote server controlled by the attacker, sending data out in small increments while the user saw nothing unusual. Microsoft patched this specific vulnerability in the January 2026 Patch Tuesday updates, but the underlying attack pattern remains relevant.
Researchers have also published tools like LOLCopilot that demonstrate how Copilot's behaviour can be altered through indirect prompt injection, enabling data exfiltration and social engineering within what appears to be a normal Copilot session.
Prompt injection defence requires a combination of platform controls and governance practices:
Microsoft's built-in protections block many prompt injection attempts, but they are not infallible. Governance adds the layers of monitoring, detection, and response that close the gap between what the platform catches and what slips through.
Your organisation is subject to a regulatory audit. The auditor asks: "How do you govern AI tool usage? Can you show us who accessed what data through Copilot, what outputs were generated, and whether any sensitive information was included in those responses?" Your IT team cannot answer. There are no Copilot-specific audit logs being reviewed, no retention policies applied to AI interactions, and no documentation of how Copilot use aligns with your data protection obligations.
This is a growing concern across regulated industries. UK GDPR requires organisations to demonstrate oversight of automated processing. The ICO expects organisations to understand how automated decisions operate under Article 22. The UAE's Personal Data Protection Law requires lawful processing with appropriate safeguards. And industry regulators like the FCA, SRA, and NHS Digital are increasingly asking about AI governance as part of standard compliance reviews.
The problem is that many compliance programmes still focus on file-level access and user actions, but not on AI-generated outputs. Copilot creates a new category of data that most retention, eDiscovery, and audit frameworks were not designed to capture.
Microsoft provides native tools for Copilot compliance, but they need to be configured and activated:
The key insight here is that compliance is not just about technology controls. It is about documentation. If you cannot show an auditor how AI is governed in your organisation, the technology controls are irrelevant.
A team lead uses Copilot in Word to draft a client proposal. Copilot helpfully pulls in relevant content from across Microsoft 365 to populate the document, including a paragraph that contains another client's confidential project details from a Teams chat, a pricing table from an old pitch that was never archived, and an internal cost breakdown that was intended for leadership eyes only. The team lead does not notice. The proposal is sent to the client.
This risk is distinct from oversharing because the data is not just viewed; it is actively reproduced and redistributed in a new document. Copilot can aggregate content from multiple sources across your tenant and synthesise it into new outputs at machine speed. Without governance controls, AI-generated content becomes a vehicle for distributing sensitive information that was never intended to leave its original context.
Preventing sensitive data from leaking through AI-generated content requires proactive classification and monitoring:
The principle is straightforward: label your data, protect your labels, and monitor what AI does with them. Microsoft Purview provides the tools. Governance provides the process that ensures they are used consistently.
Every risk in this article shares the same root cause: Copilot was deployed before governance was in place. Permissions were not reviewed. Policies were not written. Monitoring was not activated. And now the organisation is playing catch-up with an AI tool that moves faster than manual remediation ever can.
A proper AI governance framework addresses all five risks through a unified approach:
This is not about slowing down AI adoption. It is about making AI adoption sustainable, safe, and defensible. The organisations that govern first and deploy second consistently achieve better outcomes, higher user trust, and faster scaling than those that rush in and remediate later.
| Risk | Root Cause | Key Microsoft Tool | Governance Action |
|---|---|---|---|
| Data oversharing | Broad inherited permissions | SharePoint Advanced Management | Permission audit + site access reviews |
| Shadow AI agents | Ungoverned Copilot Studio agents | Power Platform Admin Centre + DLP | Managed environments + auth enforcement |
| Prompt injection | Malicious content in tenant data | Purview Communication Compliance | Monitoring + Conditional Access + patching |
| Compliance gaps | No AI-specific audit or retention | Purview Audit + eDiscovery | Retention policies + AI governance docs |
| Sensitive data in outputs | Unlabelled or unprotected content | Purview Sensitivity Labels + DSPM for AI | Auto-labelling + DLP + label inheritance |
One of the most common questions IT admins ask is which controls require which licence. Here is the breakdown:
With Microsoft 365 E3/A3/G3 (foundational controls): SharePoint data access governance reports, site access reviews, sensitivity labels (manual), DLP policies, Copilot audit logs, eDiscovery (standard), retention policies for Copilot interactions, Restricted Content Discovery.
With Microsoft 365 E5/A5/G5 (optimised controls): Everything in E3 plus auto-labelling with sensitivity labels, DSPM for AI dashboards and risk assessments, Insider Risk Management with AI-specific detection, Communication Compliance monitoring, advanced eDiscovery, Adaptive Protection that dynamically adjusts security policies based on user risk.
Most of the foundational controls that address the biggest risks, particularly oversharing and audit logging, are available in E3. You do not need E5 to start governing Copilot effectively.
Copilot security risks are not a reason to avoid deploying Copilot. They are a reason to govern it properly before deployment. Every risk in this article is solvable with Microsoft-native tools you likely already have access to. What most organisations lack is not the technology. It is the governance framework, the policies, and the structured approach that turns these tools into a coherent defence.
That is exactly what LogiSam provides.
LogiSam's AI governance services are built entirely on the Microsoft ecosystem. We help UK and UAE organisations secure their Copilot deployments with permission audits, sensitivity labelling, DLP configuration, agent governance, and a complete AI governance framework your leadership can trust.
If you have not yet deployed Copilot, start with our Copilot Readiness assessment to get your data, permissions, and governance in order before you switch it on.
Book a free consultation and let us show you what Copilot can see in your environment today.
