Shadow AI Is Already Inside Your Enterprise — Here’s How to Tell and What to Do About It
- 10Pearls Editorial Team
- 14 min read
Summary
Two out of three employees have used unauthorized AI tools at work, as per a PagerDuty report. This shadow AI – the use of unsanctioned AI tools – is a growing problem for IT and cybersecurity teams. From rapid progression and easy availability of AI tools to AI use policies in organizations that frequently lag behind, several factors contribute to alarming shadow AI statistics, including:
40%
of the companies may experience compliance or security issues stemming
from shadow AI by 2030.
(Source: Gartner)
$670,000
added to the average security breach cost for one in five organizations by shadow
AI usage.
(Source: IBM)
63%
of the companies lack AI governance policies, which
may contribute to high
shadow AI use.
(Source: IBM)
However, with the right approach to detection and prevention, shadow AI can be a very manageable IT responsibility.
What is shadow AI?
Shadow AI is both the use of unsanctioned AI tools at work and the unauthorized use of approved AI tools and capabilities. A company’s IT department is responsible for laying out the guidelines for AI usage, making a list of AI tools employees can use, and approving AI capabilities and features within existing platforms, SaaS, and enterprise systems employees already have access to.
If such a list, guidelines, and a consistent internal shadow AI definition don’t exist, virtually all AI usage may fall under the “shadow” category, and the onus of AI risk management shifts from employees to the company management and IT teams.
It’s worth noting that not all shadow AI use is inherently dangerous. Most is either benign or doesn’t produce adverse results thanks to the internal security and guardrails of the AI tools themselves. Still, the risk of shadow AI usage is quite significant, and a conscious effort to track it can help with (ideally) prevention and mitigation.
Why shadow AI is already in your enterprise
Most employees want to enhance their productivity with AI and minimal effort, so they naturally lean more towards AI tools they are already familiar with, find better aligned with their work requirements, and can easily access, regardless of whether they are approved by their IT departments or not.
So naturally, approved tools get left behind in favor of shadow AI tools employees want to use, and getting new tools approved and configured with the right security and privacy controls is a slow and tedious process in most organizations.
Some shadow AI examples are:
- Businesses heavily invested in the Google ecosystem may encourage the use of Gemini for internal code generation. Yet some developers may find personal Claude better aligned to their coding needs than company-managed Gemini.
- A company may have sanctioned a specific AI tool for document summarization, which only allows 50-page inputs in a single session. Employees may use a more generous free tool for larger documents.
- Employees in the finance department of a company may find a cutting-edge free tool much better and faster for generating reports and insights from raw financial data than company-approved models, and actively use the unsanctioned tool.
The widespread availability of free AI tools, many of which offer generous free usage limits, is another contributor. Ironically, limited usage is another factor, since it encourages users to switch from one free tool to another once they exhaust a tool’s limit.
Shadow AI vs. shadow IT
The term shadow AI can trace its genesis directly to shadow IT. Even the shadow AI meaning borrows heavily from its IT-specific predecessor, though the risk profile is different.
Shadow IT covers the use of unapproved hardware, software, online services, and mobile applications for work. While both have their own set of consequences, Shadow AI risk dimensions are both broader and more subtle than shadow IT. This includes the use of proprietary enterprise data to train AI models.
The difference between these two becomes clearer across certain dimensions:
| Dimension | Shadow IT | Shadow AI |
|---|---|---|
| What it is | Unsanctioned apps, devices, and cloud storage standing outside IT's control | Unsanctioned AI tools, plus AI features switched on inside already-approved software |
| Where the risk lands | Confidentiality and control: data sitting in unvetted apps, plus unmanaged spend and software | Confidentiality and integrity: sensitive data exposed to third-party models, and business decisions made on wrong, biased, or fabricated output |
| What the tool does with data | Stores and moves it (uploads, shares, sync) | Processes it, and depending on tier or config may retain or train on it |
| Can you see it | Largely yes: file transfers, network logs, audit trails | Harder by default: prompts use normal HTTPS with limited logging unless AI-aware tooling is in place |
| Can you contain it | Yes: it's a discrete app you can block, fence, or remove | Not cleanly: it's embedded in sanctioned tools, so it can't be firewalled the way a rogue app could |
| Who's accountable | IT, once the tool is sanctioned or blocked | Shared and unsettled: org governs access and inputs, but output-trust sits with the user and no control fully covers it |
The real risks of shadow AI
Shadow AI risks tend to be more subtle and, in many cases, more far-reaching than many organizations realize. Part of it is simply a lack of AI maturity in organizations still catching up to this rapidly evolving technology.
Some of the most critical shadow AI risk dimensions are:
Data & IP leakage
Sensitive company data, including customer/client data and internal IPs, may be absorbed by third-party, unsanctioned AI tools. This can lead to these models being trained on proprietary data or being leaked to malicious parties through prompt attacks. Based on whether it’s customer information or internal data, this risk dimension can lead to two different types of consequences.
Compliance & regulatory exposure
Shadow AI use may run against multiple regulatory requirements, including GDPR data residency rules and EU AI Act high-risk obligations. Industrial regulations like HIPAA and GLBA may also be violated due to shadow AI use. For such violations, the enterprise may be held liable directly, not the AI tool provider.
Security blind spots & expanded attack surface
Even when powered by reliable and well-known foundation models, many AI tools do not have the right guardrails and security controls in place. This makes them ripe for prompt injection, data exfiltration, and jailbreaks, expanding the attack surface. Shadow AI usage can expose company data to these tools, increasing the risk of leakage of proprietary data, private client information, and more, with IT being unaware of these AI interactions and a lack of audit trails.
Unreliable output feeding real decisions
This is not inherently a shadow AI risk because the underlying models of sanctioned tools can hallucinate too, but shadow AI significantly enhances this risk for two reasons. One is that approved models or AI tools officially adopted by the organization may be grounded in enterprise context, significantly reducing the risk of hallucinations. Secondly, unapproved tools don’t have any policy-driven governance guardrails that may prevent certain biased or misaligned outputs from passing through and influencing decision-making.
How to tell: Signs that shadow AI is in your organization.
Shadow AI management starts with identifying the signs that unsanctioned AI use is indeed taking place in your organization. These signs include:
- Unexplained productivity and even performance gains with no change in sanctioned tooling. Better reports, faster code pushes, and deeper analyses than what sanctioned AI tools support are a strong indicator of shadow AI usage.
- The productivity gap between employees/teams that are only using sanctioned tools (let’s call them the control group) and others is a strong sign of “others” dabbling in shadow AI.
- Outputs and deliverables that carry signature signs of certain AI tools. They can be challenging to detect independently because the stylistic deviation from sanctioned tools is noticeable.
- Employees discussing AI tools that are not sanctioned or suddenly asking questions about why these are not approved or when they might be approved is usually a sign that these tools are already in use.
- Cost reimbursement requests that don’t match the schedules and numbers of sanctioned tools.
spell disaster? This is where dedicated shadow AI detection tools and techniques can help.
Shadow AI detection checklist
While the exact nature and extent of shadow AI use may differ among organizations, a few shadow AI detection measures that might help most organizations are:
Watch your network and proxy traffic
Monitor outbound traffic and your web proxy for connections to known AI service endpoints, including OpenAI, Anthropic, and Google Gemini. A Cloud Access Security Broker (CASB) can help here by automatically flagging traffic to unsanctioned AI applications.
Set DLP rules to flag sensitive data in prompts
Configure Data Loss Prevention (DLP) rules to identify if sensitive data, including private consumer data and company financials, is appearing in large data blocks with well-known prompt characteristics.
Audit the logs you already have
Your SaaS admin consoles, expense systems, and SSO logs hold more than they appear to. Look for AI tools authenticated through single sign-on or subscription charges not connected to sanctioned tools, and new app authorizations no one requested through IT.
Keep an eye on browser extensions
A large share of shadow AI enters through the browser, not installed software. Catalog the extensions running on managed devices, since AI writers, summarizers, and assistants frequently arrive this way and leave almost no other trace.
Check the AI features inside tools you already approved
This is the one most detection efforts tend to miss. Sanctioned tools, including Canva and Figma, may launch AI features that were never separately approved. It’s important to deliberately track the expanding AI surface of these tools since this usage produces no new endpoint or expense.
Watch what leaves your managed devices
You cannot detect AI use on an employee’s personal laptop directly, and it’s more honest to acknowledge that than to claim otherwise. But you can still look for the traces on the managed side: sensitive files downloaded and then unaccounted for, large copy-and-paste actions off corporate endpoints, or data moving to personal cloud storage.
Run an anonymous employee survey
But make sure it’s not perceived as surveillance. If employees are reasonably confident that your goal is to understand and, ideally, rapidly sanction the tools most employees are using anyway, they will let you know about the tools honestly.
What to do about it: Building a shadow AI governance playbook
The journey from shadow AI discovery to shadow AI prevention requires a structured framework that pragmatically prevents shadow AI use without discouraging AI use. A five-step framework that answers the critical question “how to prevent Shadow AI” is:
Discover detection
Prevention starts with detection. If you follow the steps given above and look for the signs that might be unique to your workflows, industry, and region, you will have a strong starting point. It’s important to understand that detection is not simply about whether Shadow AI use is happening (spoiler: it most probably is!), but about understanding how prevalent and risky it actually is. Employees dumping entire client directories on certain AI tools may not be as unsafe as building a five-slide presentation for a potential client on another, less guard-railed tool.
Assess & classify risk
Assessing the degree of prevalence of shadow AI usage and identifying the specific tools being used can help you develop a clear risk profile. If you know what data is being shared with shadow AI tools, what those tools are, how it’s being shared (documents, directly pasted in prompts, etc.), and how frequently, you can start assigning accurate risk scores and take preventive measures. That may include blocking certain tools right away and fast-tracking others for sanctioned use.
Set a clear policy
If employees have clarity on what AI tools they can and can’t use, what data can be shared with AI tools and in which format, and how AI-generated insights and predictions can be used to inform decisions, the shadow use may lessen significantly. In the absence of a clear policy, almost all AI use is technically shadow AI. A clear policy also shifts the onus (not fully) of responsible AI use to employees.
Sanctioned alternatives
When you understand what unsanctioned AI tools employees are using and how, you can provide safer alternatives. Shadow AI security isn’t merely about stopping unsanctioned AI use but about diverting it to safe and approved tools. It’s also important that these tools are not just approved but are also grounded in enterprise context and configured for AI security and governance controls to keep AI risk to a minimum.
Monitor & educate
Even with ample sanctioned AI tools, it’s important to keep monitoring for shadow AI use because as employees become more AI curious, they may try to use the latest AI tools and functionalities just to test them out. So it’s also important to educate employees on the best practices for responsible and safe AI use and why it’s not limited to the tools they choose to work with.
From shadow AI to sanctioned AI
Preventing shadow AI use is a critical aspect of enterprise AI governance, but it has to be handled delicately. Heavy-handed shadow AI preventions or heavily restrictive AI use policies generally lead to two unwanted outcomes: Employees are discouraged from using AI, or they become better at covering their tracks when using unsanctioned AI tools.
A seasoned Artificial intelligence software development company like 10Pearls that understands both operational realities of enterprises and possesses end-to-end AI capabilities can guide you on how to prevent Shadow AI.
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