How to Find Shadow AI: 3 Hidden Entry Points
For years, companies treated shadow IT primarily as a policy and control problem.
Employees have always found and used technology outside the approved IT stack, from unauthorized endpoint applications and browser extensions to personal cloud services. It left a trail: an install, a device on the network, something a scan or an agent could eventually catch. From there, an analyst had to investigate the finding and assess the risk before deciding how to respond.
But SaaS, and now AI, have fundamentally changed the scale of the problem. Employees can adopt new applications in seconds, often without installing software, opening a ticket, or involving IT. They can connect corporate identities, grant access to business data, upload sensitive information, and begin using powerful AI tools before security teams even know those applications exist.
According to the WatchGuard Cybersecurity Hygiene Report 2026, 64% of employees report using unsanctioned AI tools for work tasks.
MSPs know Shadow AI is already present in their clients' environments. What they often lack is the capability to discover which applications employees use, detect when those applications pose risk, and respond before unauthorized access or data exposure becomes a security incident.
Most unauthorized AI and SaaS applications enter through one of three common entry points. Each leaves evidence in a different place and requires a different approach to detection.
Entry Point 1: Employees Grant Access to Company Data
The most common entry point starts with a simple click.
An employee selects "Sign in with Microsoft" or "Continue with Google" to connect an AI assistant, productivity tool, or SaaS application to company resources.
Within seconds, that application may gain access to email, calendars, files, contacts, or other business data. Those permissions often remain in place long after the original business need ends.
Attackers also exploit this same process through consent phishing. Instead of stealing passwords, they trick users into granting access directly.
Organizations can't manage application access that they don't know exists. The first step is identifying which applications already connect to company data.
Entry Point 2: Employees Use Personal Accounts
Not every AI tool connects through a company's identity platform.
Many employees simply open a browser, visit an AI service, and sign in with a personal account.
The activity doesn't appear in Microsoft 365 or Google Workspace because the employee never used a corporate identity. As a result, organizations often can't see what information employees upload, share, or process through the service.
Without visibility into those activities, organizations can't accurately assess risk or enforce governance policies.
Entry Point 3: The Only Evidence Is Network Activity
The third entry point leaves almost no obvious trail.
An employee accesses an AI or SaaS service directly from a company-managed device. The activity never creates an application permission in the tenant and never authenticates through a corporate identity provider.
In many cases, the only evidence appears in network or firewall logs.
These connections blend in with ordinary web traffic. Without additional context, they're difficult to identify.
As a result, potentially risky AI use often goes unnoticed amid thousands of routine network connections.
Why Visibility Across All Three Entry Points Matters
Each entry point leaves evidence in a different location:
- Identity platforms reveal OAuth permissions and access to third-party applications.
- User activity exposes AI services used outside corporate identities.
- Network traffic identifies services that leave no trace elsewhere.
Security teams need visibility into all three entry points to discover Shadow AI activity and respond appropriately.
Closing the Shadow AI Visibility Gap
Closing the Shadow AI visibility gap requires looking beyond any single security control or telemetry source. Identity and application permissions can reveal which services have been granted access to corporate data, but they won't capture AI tools used through personal accounts or services that never request those permissions. Endpoint and network activity can reveal additional application usage, but may not reveal the access an application has been granted within a SaaS environment.
Effective Shadow AI governance requires bringing these signals together to answer three fundamental questions:
- What applications are employees using?
- What corporate data and resources can those applications access?
- Which activities create enough risk to require action?
A longer inventory of AI tools has limited value on its own. Continuous visibility across identity, applications, devices, and network activity lets security teams distinguish legitimate AI adoption from activity that creates unnecessary exposure and respond proportionately to the risk.
Detect Shadow AI with WatchGuard
WatchGuard helps MSPs discover, detect, and respond to Shadow AI across multiple visibility sources. Using CloudDR, FireCloud, and Firebox for Shadow AI risks, security teams can identify which applications are in use, what data they can access, and whether they pose a risk.
- CloudDR, WatchGuard's cloud detection and response solution, discovers applications connected via OAuth permissions across Microsoft 365, Google Workspace, and other supported SaaS platforms. It surfaces consent grants to identify suspicious access, including consent-phishing attempts, and revokes permissions before exposure becomes an incident. It also helps identify services employees sign up for using corporate email addresses, even when those services aren't connected to a corporate identity.
- With FireCloud Total Access, MSPs can extend visibility by collecting network telemetry from managed devices. This allows organizations to identify cloud services and AI applications regardless of whether users authenticate with Microsoft 365, Google Workspace, or another SaaS application.
- For organizations using Firebox, firewall telemetry provides another source of insight. Rather than inspecting content, Firebox identifies connections to known AI destinations and surfaces that activity for review.
Together, these solutions offer MSPs a more complete picture of Shadow AI across client environments. Organizations can identify connected applications, understand what data they can access, assess risk, and take action when necessary.
These capabilities also map to compliance frameworks, including HIPAA, PCI DSS, ISO 27001, GDPR, and SOC 2. Security findings turn into audit-ready evidence. Application inventories now cover Shadow AI risk, and MSPs can bundle or add-on compliance reporting services to that coverage.
The Cost of Not Knowing
The consequences of Shadow AI extend beyond visibility gaps. When organizations don't know which AI applications employees use or what data those applications can access, the business impact can be significant.
According to the IBM Cost of a Data Breach Report 2026, incidents involving shadow AI cost organizations an average of $5.39 million, exceeding the global average breach cost of $4.99 million.
Many of these incidents begin with routine employee behavior: granting an application access to company data, using a personal AI account on a work device, or accessing an unapproved service through a browser. Without visibility into these activities, risks can remain undetected until after an incident occurs.
MSPs and internal IT teams don't need to stop AI adoption. They need to understand which applications employees use, how those applications were introduced into the environment, and what data they can access. Organizations that have that visibility are better positioned to manage risk, support compliance requirements, and respond quickly when issues arise. You can't manage what you can't see.
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