Shadow AI: The New Shadow IT

For years, organizations have struggled with shadow IT—unapproved systems, tools, or cloud services deployed outside normal governance.
In 2025, that problem quietly evolved into something far more complex and far more dangerous:

Shadow AI.

Employees are now spinning up unapproved AI agents, connecting them to sensitive data, granting them excessive permissions, or even embedding them into workflows without the organization’s knowledge. And because AI is both powerful and opaque, many of these risks remain invisible until something goes wrong.

This post explores what shadow AI looks like, why it’s emerging so quickly, and what defenders and leaders can do right now to reduce the growing risk.

What Is Shadow AI?

Shadow AI refers to the use of unapproved, unmanaged, or unsanctioned AI systems within an organization, often introduced by well-meaning employees solving real problems.

Shadow AI most often appears as:

  • Personal GPT agents used for work tasks
  • Browser extensions with hidden LLM integrations
  • AI plug-ins attached to cloud platforms
  • Team-created automation scripts with embedded model calls
  • Data uploaded into public models “just to get something done”
  • Locally run LLMs used for sensitive analysis
  • AI copilots generating or modifying production code
  • Autonomous agents connected to internal systems via API keys

In many cases, employees believe they are improving efficiency, not increasing organizational risk. That’s what makes shadow AI so difficult to detect.

Why Shadow AI Is Growing So Quickly

Three major forces are driving its growth:

1. AI Tools Are Too Easy to Access

Anyone can start an agent in seconds—no install, no approval process, no oversight.

2. LLM Integrations Are Hidden in Everyday Software

Products now include embedded AI features that quietly send data to third-party models.

3. Employees Are Under Pressure to Work Faster

AI promises productivity gains. Many users adopt tools first and ask permission later.

The Real Risks of Shadow AI

Shadow AI isn’t just a governance issue. It creates new attack surfaces and undermines traditional security assumptions.

1. Data Leakage Into AI Model Memory

Employees upload logs, customer data, internal code, PII, or financial documents into third-party models.

2. Excessive Permissions Granted to AI Plug-ins

Browser AI extensions often request full read access to tabs, cookies, and forms.

3. AI Agents Acting Autonomously

Some tools can send emails, access APIs, write files, or make decisions without human approval.

4. Supply Chain Risk in AI Models

Models and plug-ins can be tampered with, poisoned, or modified without visibility.

5. Code Generated by AI Introduces Silent Vulnerabilities

Developers rely heavily on AI suggestions, which may include insecure patterns or outdated libraries.

6. Loss of Auditability

If work is done through AI agents rather than sanctioned systems, visibility disappears.

What Shadow AI Teaches Us About Modern Security

Shadow AI is highlighting something defenders already suspected:

Identity, not infrastructure, is now the true perimeter.

When users can authorize AI agents using their own identity tokens, the organization inherits risk from:

  • OAuth scope misconfigurations
  • Refresh token theft
  • API permissions granted without review
  • Autonomous actions taken on behalf of the user

Traditional security controls were never designed for this model.

How Organizations Can Reduce Shadow AI Risk Right Now

You don’t eliminate shadow AI by banning it. You eliminate it by making approved AI easier and safer to use than the alternatives.

1. Publish a Clear AI Usage Policy

Employees need clarity, not fear. Include:

  • Acceptable data types
  • Disallowed tools
  • Approved tools
  • Logging expectations

2. Offer a Sanctioned AI Platform

Provide:

  • A vetted LLM
  • Centralized logging
  • Data loss controls
  • API gateways for AI workloads

If employees have a safe option, shadow tools drop naturally.

3. Audit Permissions on AI Plug-Ins and Extensions

Some extensions request highly privileged access to:

  • Browser cookies
  • Network data
  • Screenshots
  • Cloud token scopes

Many organizations never check.

4. Leverage Zero Trust for AI Workloads

Treat AI agents as their own identities—never as proxies for users:

  • Least-privilege API scopes
  • Segmented data stores
  • Continuous monitoring

5. Train Teams on AI Risks

Most shadow AI use is not malicious.
It’s a training problem, not a disciplinary problem.

Where Shadow AI Fits Into the Future of Cybersecurity

Shadow AI is not a temporary issue—it’s the next phase of enterprise risk. Just as shadow IT forced organizations to modernize cloud governance, shadow AI will force:

  • AI security baselines
  • Model supply chain verification
  • AI-aware incident response
  • Identity-first architecture
  • Context-based data protection

Organizations that adopt AI without adopting AI governance will be exposed to significant and often invisible vulnerabilities.

Shadow AI isn’t going away. The only question is whether organizations will address it proactively or reactively.

Final Thoughts

Shadow AI represents a fundamental shift in how organizations work and how they must defend themselves.
It blends human behavior, emerging technology, and identity-driven risk in ways traditional security controls were never designed to manage.

Security teams that acknowledge and understand the rise of shadow AI will be far better prepared for the evolving threat landscape. Those that ignore it may struggle to understand where their data is going, who has access to it, and which systems are making decisions on their behalf.

Shadow AI is the new shadow IT. And it’s already here.