Before You Deploy Copilot at Your Bank, Read Microsoft's Own Security Warning

In January 2026, Microsoft confirmed that Copilot — their own AI assistant — was bypassing Data Loss Prevention policies and summarizing confidential emails. Emails with sensitivity labels applied. Emails that DLP rules were supposed to protect. Copilot read them anyway and handed summaries to users who shouldn't have seen the content.

Microsoft tracked it as case CW1226324. They called it a code logic error and deployed a server-side fix. But think about what happened: Microsoft's AI tool broke Microsoft's security controls. Inside Microsoft's own platform. For financial institutions, that means any environment with Copilot licenses assigned could have had loan committee notes, board communications, or member account data surfaced through a pathway that DLP was supposed to block.

Then in February 2026, Microsoft's own security team published a document titled "Copilot Studio Agent Security: Top 10 Risks." Not a competitor's analysis. Not a third-party audit. Microsoft's security researchers telling their own customers: here are the ways AI agents in your environment can go wrong.

If you run a bank, credit union, or mortgage company and your team is evaluating Microsoft Copilot, both of these should be required reading before anyone approves a pilot. The DLP bypass proved that even properly configured controls can fail. And the risks Microsoft identified in their Top 10 land differently when the data in your environment includes Social Security numbers, loan applications, and member account records.

What Microsoft's Security Team Published

Microsoft released the Copilot Studio Agent Security Top 10 on February 12, 2026. It identifies the ten most significant security risks when organizations build and deploy AI agents using Copilot Studio.

The key risks include:

• Hard-coded credentials in agent configurations. Developers embedding API keys, service account passwords, or connection strings directly into agent workflows. These credentials become accessible to anyone who can inspect the agent configuration.
• Over-permissioning. Agents granted broader data access than their function requires. An agent built to answer HR questions doesn't need access to financial records, but if permissions aren't scoped correctly, it can reach everything the underlying service account can reach.
• Unreviewed MCP tools and connectors. Third-party integrations connected to agents without security review. Each connector expands the attack surface and the data exposure footprint.
• Tool invocation exploits. Prompt injection attacks that trick agents into calling tools or accessing data they shouldn't. An attacker crafts input that causes the agent to bypass its intended boundaries.
• Data exfiltration through agent responses. Agents that surface sensitive data in responses to users who shouldn't have access, or that leak data through conversation logs.

Microsoft's recommendation is direct: treat every AI agent as a first-class identity with human-equivalent governance. That means access reviews, permission scoping, audit logging, and security monitoring, the same controls you'd apply to a human employee with the same data access.

The Risks That Matter Most for Financial Institutions

Every risk on Microsoft's list is relevant to financial services, but three of them create specifically regulatory problems.

Over-Permissioning and Data Exposure

Most employees at financial institutions are over-permissioned. They have access to SharePoint sites, Teams channels, and email folders that go beyond what their job function requires. In a pre-Copilot world, that's a manageable risk because employees don't actively browse every file they can technically access.

Copilot changes that equation. It surfaces all data the employee has access to. Ask Copilot a question, and it searches across every SharePoint site, every email, every Teams message the user's permissions allow. Data that was technically accessible but practically hidden becomes instantly retrievable.

For a bank, that means a teller asking Copilot a question could get results that include board meeting minutes, loan committee notes, or HR files if the permission boundaries aren't right. The data was always there. Copilot just made it findable.

Audit Trail Requirements

Financial regulators expect audit trails for data access. Who accessed what, when, and why. AI agents introduce a new category of data access that most institutions haven't incorporated into their audit framework.

When a Copilot agent retrieves member data to answer a question, that access needs to be logged, reviewable, and attributable. If your examiner asks "who accessed this member's loan records in the last 90 days" and the answer includes "an AI agent, but we don't have detailed logs," that's a compliance gap.

Regulatory Risk of AI-Generated Content

If a Copilot agent generates a response to a member inquiry that contains incorrect information about their account, loan terms, or regulatory rights, your institution is responsible for that content. AI-generated responses carry the same regulatory weight as human-generated responses when they reach a customer.

The Data Oversharing Problem

The core issue isn't Copilot itself. It's what Copilot reveals about your existing data governance.

Most financial institutions have permission sprawl. Over the years, SharePoint sites get shared broadly. Teams channels accumulate members. Email distribution groups expand. Nobody cleans up access when people change roles or leave the organization.

Before Copilot, this was a background risk. After Copilot, it's an active exposure. Every over-permissioned user now has an AI assistant that can surface any data within their access boundary in seconds.

Microsoft offers Purview Suite for Copilot (currently at 50% discount through June 2026) specifically to address this problem. Purview provides data classification, sensitivity labeling, and access governance tools that let you define what data Copilot can and can't surface.

But deploying Purview is a data governance project, not a software installation. You need to classify your data, define sensitivity labels, apply those labels consistently, and then configure Copilot's access boundaries around them. For a financial institution with thousands of documents containing member personally identifiable information (PII), that's a significant undertaking.

What to Do Before You Deploy Copilot

If your institution is evaluating Copilot, these steps should happen before the pilot begins, not after:

1. Permission Audit

Review who has access to what across your Microsoft 365 environment. SharePoint site permissions, Teams channel memberships, OneDrive sharing settings, email distribution groups. Identify and remediate over-permissioning before Copilot amplifies it.

2. Data Classification

Classify your data by sensitivity level. Member PII, loan records, board materials, financial reports, and HR documents all need explicit sensitivity labels. These labels govern what Copilot can surface and to whom.

3. Conditional Access Review

Your existing Microsoft 365 security configuration needs to account for Copilot's access patterns. Conditional Access policies should include conditions for AI-assisted data retrieval, not just direct user access.

4. Audit Logging Configuration

Enable and configure audit logging for Copilot interactions. Every query, every data retrieval, every response should be logged in a format your compliance team can review and your examiner can audit.

5. AI Use Policy

Document your institution's AI acceptable use policy. Which roles can use Copilot? For which tasks? What data categories are off-limits? What review process applies to AI-generated customer communications? Your policy should build on identity security fundamentals that are already in place. Your examiner will ask about this policy.

Building an AI Governance Framework for Regulated Financial Institutions

Microsoft's Copilot Control System provides monitoring for oversharing, anomalous behavior, and potential misuse. But the technology is only one piece. Your institution needs a governance framework that addresses:

• Identity governance for AI agents. Every Copilot instance and custom agent should be inventoried, scoped, and monitored like a human identity. Regular access reviews, least-privilege permissions, and deprovisioning processes when agents are retired.
• Data governance prerequisites. Copilot is only as safe as your data classification. If you haven't classified your data and applied sensitivity labels, Copilot will surface everything. Classification must happen before deployment, not concurrently.
• Incident response updates. Your incident response plan needs an AI section. What happens when a Copilot agent surfaces data it shouldn't? When a prompt injection attack succeeds? When AI-generated content reaches a customer with incorrect information? These scenarios need documented procedures.
• Board reporting. Your board should understand the risk profile of AI deployment. A clear, non-technical report on what AI tools are deployed, what data they access, what controls are in place, and what incidents have occurred is becoming a governance expectation.

The institutions that deploy Copilot with proper governance will gain a genuine operational advantage. The ones that deploy it without governance will hand their examiner a new category of findings. A provider that understands financial services compliance can help you build the governance framework before the deployment, not after the examination.

Frequently Asked Questions

Technical Reference

Glossary

Term	Definition
Conditional Access	Microsoft Entra ID policies that control who can access resources based on identity, device, location, and risk signals. Used to enforce multi-factor authentication and block unauthorized access patterns.
Copilot Studio	Microsoft's platform for building custom AI agents that can access organizational data, call external services, and automate workflows within the Microsoft 365 ecosystem.
DLP (Data Loss Prevention)	Microsoft Purview policies that detect and block sensitive data — such as Social Security numbers and account numbers — from leaving the organization through email, Teams, or file sharing.
DMARC / DKIM / SPF	Email authentication protocols that verify sender identity and prevent domain spoofing. DMARC (Domain-based Message Authentication, Reporting, and Conformance) builds on DKIM and SPF to provide enforcement and reporting.
FFIEC	Federal Financial Institutions Examination Council. Interagency body that sets IT examination standards for banks and credit unions, including the Cybersecurity Assessment Tool used by examiners.
MFA (Multi-Factor Authentication)	Security control requiring two or more verification methods to access a system. Typically combines something you know (password), something you have (phone or security key), and something you are (biometrics).
Microsoft Purview	Microsoft's data governance suite providing data classification, sensitivity labeling, DLP, and compliance tools. Required for controlling what data Copilot can access and surface.
NCUA	National Credit Union Administration. Federal regulator that examines credit unions for safety and soundness, including cybersecurity and data governance practices.
PII (Personally Identifiable Information)	Data that can identify an individual, including Social Security numbers, account numbers, loan records, and contact information. Subject to strict handling requirements under financial regulations.
Prompt Injection	An attack technique where crafted input tricks an AI agent into performing unauthorized actions, accessing restricted data, or bypassing security boundaries.
Sensitivity Labels	Microsoft Purview classification tags applied to documents and data that enforce protection rules — such as encryption, access restrictions, and watermarking — based on the content's sensitivity level.