2026 Microsoft Copilot Security Concerns Explained

Refreshed and updated on April 23rd, 2026.

Key Takeaways:

• Copilot’s biggest security risk is overly permissive data access. Copilot can access everything a user can within Microsoft 365, and Concentric AI’s Data Risk Report found that 16% of business-critical data is overshared, with an average of 802 thousand files at risk per organization.
• Copilot outputs don’t consistently inherit security labels from source files. This means sensitive data generated or surfaced by Copilot can end up unclassified and improperly shared, putting the burden on employees to catch and fix classification gaps.
• Organizations need to audit permissions and address data risk before deploying Copilot. Without a clear understanding of who has access to what, Copilot will amplify existing over-permission problems across finance, HR, R&D, marketing, and other departments.
• Concentric AI helps secure Copilot by autonomously classifying AI-generated output, identifying risk from inappropriate access or sharing, and remediating issues without requiring rules, regex, or end-user involvement.

You’ve used ChatGPT and you already know how powerful and helpful it can be.

But today, ChatGPT is only one of many tools for generative AI. In late 2023, Microsoft rolled out its own AI chatbot for the enterprise called Copilot. Google also introduced Gemini (formerly Bard), which also has significant benefits and drawbacks.

In 2025, Copilot has already been deployed in many organizations. But it hasn’t been an easy ride.

In March, the U.S. House of Representatives banned congressional staff from using Copilot due to concerns about data security and the potential risk of leaking House data to unauthorized cloud services. In June, Microsoft announced it was discontinuing the GPT Builder feature within Copilot Pro, which allowed users to create custom Copilot GPTs. Also in June, in response to backlash over the Recall feature, which captured and stored screenshots for AI analysis, Microsoft decided to make it an opt-in function rather than enabling it by default.

The reality is generative AI tools like Copilot can be wonderful for productivity but introduce some notable red flags for the enterprise.

TLDR; Three things you need to know about Copilot security

If you don’t have time to read the entire blog, here are the key things you need to know:

• Copilot has overly broad permissions

One of the primary concerns with Microsoft Copilot is its potential for over-permissioning, which can lead to unintended data access across an organization. As a generative AI tool, Copilot aggregates data from Microsoft 365, potentially creating vulnerabilities if permissions aren’t carefully restricted. Mismanaged permissions can result in widespread, often inadvertent access to confidential files, including intellectual property, financial documents, and personal information, underscoring the importance of vigilant data governance.

2. The risks of data exposure are significant 

With the integration of AI, there’s a heightened risk of sensitive data being accessed unintentionally. Copilot’s advanced features allow it to handle significant amounts of data, but without strict access controls, this same functionality can increase exposure risks. Data mishandling incidents or unauthorized data sharing can occur, impacting compliance and security.

3. Organizations need proactive access management

To mitigate the risks, organizations must implement proactive access management measures — before, during and after Copilot deployment. This includes regularly reviewing permission settings within Microsoft 365, conducting data audits, and ensuring users only access data essential to their roles. Proactive management also involves using tools to identify and correct over-permissioned accounts to maintain optimal data security and limit exposure.

What is Microsoft Copilot? 

Imagine having a personal AI assistant tucked into every Microsoft 365 app you use – from Word and Excel to Teams and Outlook. The purpose of Copilot is to strip away the tedious bits of an employee’s workday so they can be more productive and creative than ever.

What differentiates Copilot from other AI tools like ChatGPT is its ability to deep dive into your organization’s Microsoft 365 content. Think of Copilot as having a 24/7 virtual assistant who can quickly and efficiently remember every bit of your work and whip up a summary, spreadsheet or document quickly and efficiently.

The potential for Copilot is endless; the expected productivity surge may exceed that of ChatGPT. Simply open a new Word doc, tell Copilot to draft a client proposal using elements from your notes and past presentations, and you’ve got a complete proposal in seconds.

Copilot can even summarize Teams meetings, keeping track of the key points and to-dos. It can also be your email wingman in Outlook, helping you sort through your inbox. In Excel, it becomes your data analyst.

How does Copilot work? 

If you’ve been using Microsoft products long enough, you’ll remember Clippy. Copilot is as easy as Clippy, but much less annoying and with much better results.

With Copilot, the process is very simple and works like ChatGPT:

• Open the app/sidebar and type in a prompt
• Microsoft looks at your 365 permissions to get the context
• The prompt is sent to the Large Language Model (think GPT-4) to do its AI magic
• Microsoft puts it through a responsible AI check

Sounds great! What’s the catch? Copilot risks explained

What are the common Copilot security risks?

In theory, Copilot is a dream come true. But there are security risks that must be addressed. While Microsoft does its best to keep security in mind with its product, data security teams need to know this: Copilot essentially has the keys to the kingdom. It can access all the sensitive data you can, which – to be honest – is sometimes more than it should.

Plus, Copilot can do more than fetch data; it can create new sensitive data quickly and in large quantity.

The big issue here is overly permissive data access, which happens in organizations far more often than you think.

Here at Concentric AI, we publish a Data Risk report twice a year based on our comprehensive findings. Using advanced AI capabilities, Concentric AI processed over 500 million unstructured data records and files from companies in the technology, financial, energy and healthcare sectors. This report underscores the risk to unstructured data in the real world by categorizing the data, evaluating business criticality, and accurately assessing risk.

Our most recent report analyzed over 550 million data records and found that 16% of an organization’s business-critical data is overshared. That adds up to a lot of data: on average, organizations have 802 thousand files at risk due to oversharing.

Let’s explore some other staggering statistics:

• 83% of the at-risk files were overshared with users or groups within the company
• 17% were overshared with external 3rd parties
• 90% of business‐critical documents are shared outside the C‐suite
• Over 15% of all business-critical files are at risk from oversharing, erroneous access permissions and inappropriate classification and so can be seen by internal or external users who should not have access
• Over 3% of business sensitive data was shared organization wide without concern for whether it should have been shared or not

What about Copilot’s access to sensitive data?  

Let’s assume Copilot becomes as embraced by the enterprise as many expect. In that case, companies must approach its use like any other application: walking a tightrope between productivity and restricting access to employees who need it to do their work. Remember the rush to get employees going with access to work remotely during the pandemic? It was a massive challenge to set permissions and security settings promptly.

The good news is Copilot will only work with your M365 tenant data and won’t be able to access other companies’ data. Plus, your data doesn’t train the AI for other companies to leverage.

However — there are several issues:

• Copilot will leverage all the data that the employee has access to; from our data risk report, it is clear that most employees’ permissions to sensitive data are far greater than what they should be entitled to
• Copilot results do not inherit the security labels from the source files. This represents a serious risk for source files containing sensitive data.
• Now, it’s up to the employee to double-check the AI’s work and ensure data is classified and assessed for risk properly.

Here’s what Microsoft says about access rights in its Copilot data security documentation: “It’s important that you’re using the permission models available in Microsoft 365 services, such as SharePoint, to help ensure the right users or groups have the right access to the right content within your organization.”

When it comes to permissions in the ideal world, zero trust is always best, where, like in the CIA, access to information is on a need-to-know basis. Microsoft suggests using M365’s permission models to keep things locked down, but in reality, most setups are far from that ideal.

As for the labels and classification methods that companies rely on to keep data protected, they can get messy, and AI-generated data will only make it messier. With so much data to manage, organizations should not expect their employees to be perfect stewards of data risk. We know it’s hard enough for security teams.

What’s changed since 2025: agentic AI and the expanding attack surface

The security story for Microsoft Copilot keeps changing. A year ago, the primary concern was a human employee using Copilot to surface data they had no business seeing. That risk still stands. 

But the threat surface has grown considerably, because Copilot is no longer (and maybe never was) just a chat assistant.

Microsoft has been steadily rolling out agentic capabilities, with Copilot agents that autonomously perform tasks on your behalf, like drafting emails, scheduling meetings, querying data. They can also trigger actions across M365 without a human initiating each step. As organizations adopt agentic AI, any visibility and security gaps increase the risk of the agents becoming catalysts for data exposure. They’re operating across workflows using real identities, real permissions, and real data stores. 

Attack categories are being introduced that traditional DLP and access controls weren’t built for.

The EchoLeak vulnerability. In June 2025, researchers at Aim Security disclosed what they called the first “zero-click” attack on an AI agent. The vulnerability allowed an attacker to trigger a Copilot exploit simply by sending an email to a user with no click, phishing, or malware required. It could pull sensitive data from connected M365 sources in the process.

Microsoft patched it quickly, but the researchers were direct about the broader implication: EchoLeak reflects a design-level challenge in how LLM-based agents process and act on content, not a one-off coding error. The bug gets fixed but the underlying dynamic doesn’t go away on its own.

Prompt injection at scale. Malicious instructions hidden in ordinary business content—an email, a shared document, a Teams message—can be interpreted by the LLM as commands. In one documented exploit chain, Copilot searched a victim’s emails without confirmation, encoded the retrieved data invisibly, and presented a link that silently transmitted the content to an attacker’s server. While traditional security tools monitor file access, they can’t monitor what an AI agent is being told to do by the content it processes.

Agent misconfigurations in Copilot Studio. In real environments, Microsoft’s own security research repeatedly finds well-intentioned configuration choices becoming security gaps. A few examples include agents shared too broadly, data exposed without authentication, agents running with excessive privileges, or credentials stored directly in agent definitions.  

These risks rarely look dangerous until someone exploits them.

The OWASP Top 10 for Agentic Applications (2026). OWASP, the organization that defined application security baselines for the last two decades, published a dedicated top 10 list for agentic AI systems in 2026. The risks include unsafe tool invocation, uncontrolled external dependencies, and privilege escalation across LLM trust boundaries. 

Microsoft has responded with new infrastructure: Agent 365, generally available May 1, 2026, adds runtime threat protection and agent identity management through Entra Agent ID, priced at $15 per user per month on top of existing M365 licenses. 

That Microsoft built a dedicated product to govern agent behavior is a strong indicator that a gap existed before .

None of this replaces the original data access problem; it adds to it. Every new agent you deploy in Copilot Studio inherits the same over-permissioned data estate that the rest of your users do. And the agents can now act on the data  autonomously, at speed, without asking.

What are some real-world examples of Copilot security risks?  

Let’s explore four potential scenarios that are bound to happen across organizational departments in finance, HR, R&D, and marketing.

Finance: A financial analyst uses Copilot to generate a quarterly financial report. The input data includes a mix of public financial figures and sensitive, unreleased earnings data. Due to an oversight, the sensitive data is not correctly classified at the input stage. Then, Copilot generates a comprehensive report that includes sensitive earnings data but fails to classify it as confidential. What if this report was inadvertently shared with external stakeholder?

HR: An HR manager uses Copilot to compile an internal report on employee performance, including personal employee information. The source data has overly permissive access controls, allowing any department member to view all employee records. What if the Copilot-generated report inherits these permissions, and sensitive employee information becomes accessible to all department members, violating privacy policies? It could potentially lead to internal chaos and legal challenges.

R&D: A product development team uses Copilot to brainstorm new product ideas based on existing intellectual property (IP) and R&D data. The team’s input includes confidential information about upcoming patents. Copilot, lacking context on the company’s sensitivity towards this IP, incorporates detailed descriptions of these patents in its output. What if this output is then shared with a broader audience, including external partners, inadvertently exposing future product plans and risking IP theft?

Marketing: A marketing team uses Copilot to analyze customer feedback and generate a report on customer satisfaction trends. The input data contains sensitive customer information, including criticism of unreleased products. Since Copilot outputs are unclassified by default, the generated report does not flag the sensitive customer feedback as confidential. What if the report is uploaded to a shared company server without appropriate access restrictions, making the critical feedback—and details about the unreleased products—accessible to unauthorized employees? Internal leaks and competitive disadvantage become a significant risk.

If you’re a Microsoft shop, Purview alone can’t do it all

It’s clear that Microsoft has added meaningful security controls for Copilot since its initial deployment. Purview now supports DLP policies for Copilot prompts, sensitivity label inheritance for agents, insider risk management extensions, and data life cycle controls for agent-generated content. Yes, they belong in your security stack.

But a lot of enterprise security teams don’t understand an important qualification. Purview governs what you’ve already labeled. It enforces policies against data that already has been classified correctly. But what it won’t do is find what was missed.

And as Concentric AI’s own Data Risk Report shows, the typical enterprise has missed a lot. Sixteen percent of business-critical data is overshared. Over 800,000 files per organization sit at risk. Most of those files have inaccurate sensitivity labels—or they aren’t labeled at all, which is exactly why they’re at risk in the first place.

It’s a Catch-22: you deploy Copilot, Copilot queries your M365 estate, and Purview enforces your existing labels. But the files that represent the greatest exposure are the ones that never got labeled properly. 

Purview has nothing to enforce against the unlabeled files , and Copilot serves them up freely.

Copilot amplifies whatever already exists–good or bad. If your data classification is clean, Purview and Copilot together work reasonably well. If it’s a mess, you’re just governing the mess and not fixing it.

This is not a criticism of Microsoft’s tooling. It’s more an acknowledgment of what any rules-based, label-dependent system can and cannot do. Purview is built to enforce, but it can’t discover and autonomously understand the sensitivity of data that has never been given a label.

How to keep sensitive data safe with Concentric AI 

Classification challenges 

To best manage any type of data risk, sensitive information — from financial data to PII/PHI/PCI to intellectual property to confidential business information —needs to be identified, classified and remediated if at risk. Remember, sensitive data can be stored in the cloud, on premises, structured or unstructured data.

While most classification methods are better than having none at all, most paths to classification — like end-user, centralized and metadata-driven — can be time-consuming, ineffective and full of unnecessary obstacles.

As difficult as this sounds, organizations need to have a clear understanding of data risk before fully deploying Copilot.

How Concentric AI helps secure Copilot  

Concentric AI leverages sophisticated natural language processing capabilities to accurately and autonomously categorize data output from Copilot into categories that include privacy-sensitive data, intellectual property, financial information, legal agreements, human resources files, sales strategies, partnership plans and other business-critical information.

Concentric AI can analyze the output from Copilot to discover sensitive information – from financial data to PII/PCI/PHI — and label the data accordingly to ensure that only authorized personnel have access to it.

This also ensures that employees don’t have to worry about labeling the output, resulting in better security.

Once that data has been identified and classified, Concentric AI can autonomously identify risk from inappropriate permissioning, risky sharing, unauthorized access, wrong location etc.

Remediation actions, such as changing entitlements, adjusting access controls, or preventing the data from being shared, can also be taken centrally to fix issues and prevent data loss.

Best of all, Concentric AI can help you address Copilot’s security risks without having to write a single rule.

To sum up, with Concentric AI, your organization can effectively manage generative AI output data:

• Discover, monitor and protect all data types, including cloud, on-premises, structured, unstructured, and shared via messaging services
• Auto-label sensitive data output from Copilot
• Gain a risk-based view of data and users
• Leverage automated remediation to instantly fix access and activity violations
• Find risk without rules, formal policies, regex, or end-user involvement
• Secure API-based SaaS solution with no agents required

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