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* Products  
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  * [SaaS Security](https://www.paloaltonetworks.com/sase/saas-security?ts=markdown)  
    [AI-Driven Security Operations Platform](https://www.paloaltonetworks.com/cortex?ts=markdown)
  
  * [Cloud Security](https://www.paloaltonetworks.com/cortex/cloud?ts=markdown)
  
  * [Cortex Cloud](https://www.paloaltonetworks.com/cortex/cloud?ts=markdown)
  
  * [Application Security](https://www.paloaltonetworks.com/cortex/cloud/application-security?ts=markdown)
  
  * [Cloud Posture Security](https://www.paloaltonetworks.com/cortex/cloud/cloud-posture-security?ts=markdown)
  
  * [Cloud Runtime Security](https://www.paloaltonetworks.com/cortex/cloud/runtime-security?ts=markdown)
  
  * [Prisma Cloud](https://www.paloaltonetworks.com/prisma/cloud?ts=markdown)
  
  * [AI-Driven SOC](https://www.paloaltonetworks.com/cortex?ts=markdown)
  
  * [Cortex XSIAM](https://www.paloaltonetworks.com/cortex/cortex-xsiam?ts=markdown)
  
  * [Cortex XDR](https://www.paloaltonetworks.com/cortex/cortex-xdr?ts=markdown)
  
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  * [Cortex XSOAR](https://www.paloaltonetworks.com/cortex/cortex-xsoar?ts=markdown)
  
  * [Cortex Exposure Management](https://www.paloaltonetworks.com/cortex/exposure-management?ts=markdown)
  
  * [Cortex Xpanse](https://www.paloaltonetworks.com/cortex/cortex-xpanse?ts=markdown)
  
  * [Cortex Advanced Email Security](https://www.paloaltonetworks.com/cortex/advanced-email-security?ts=markdown)
  
  * [Unit 42 Managed Detection \& Response](https://www.paloaltonetworks.com/cortex/managed-detection-and-response?ts=markdown)
  
  * [Unit 42 Managed XSIAM](https://www.paloaltonetworks.com/cortex/managed-xsiam?ts=markdown)

* Solutions  
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  Secure AI by Design
  
  * [Secure AI Ecosystem](https://www.paloaltonetworks.com/prisma/prisma-ai-runtime-security?ts=markdown)
  * [Secure GenAI Usage](https://www.paloaltonetworks.com/sase/ai-access-security?ts=markdown)  
    Network Security
  * [Cloud Network Security](https://www.paloaltonetworks.com/network-security/software-firewalls?ts=markdown)
  * [Data Center Security](https://www.paloaltonetworks.com/network-security/data-center?ts=markdown)
  * [DNS Security](https://www.paloaltonetworks.com/network-security/advanced-dns-security?ts=markdown)
  * [Intrusion Detection and Prevention](https://www.paloaltonetworks.com/network-security/advanced-threat-prevention?ts=markdown)
  * [Device Security](https://www.paloaltonetworks.com/network-security/device-security?ts=markdown)
  * [OT Security](https://www.paloaltonetworks.com/network-security/ot-device-security?ts=markdown)
  * [5G Security](https://www.paloaltonetworks.com/network-security/5g-security?ts=markdown)
  * [Secure All Apps, Users and Locations](https://www.paloaltonetworks.com/sase/secure-users-data-apps-devices?ts=markdown)
  * [Secure Branch Transformation](https://www.paloaltonetworks.com/sase/secure-branch-transformation?ts=markdown)
  * [Secure Work on Any Device](https://www.paloaltonetworks.com/sase/secure-work-on-any-device?ts=markdown)
  * [VPN Replacement](https://www.paloaltonetworks.com/sase/vpn-replacement-for-secure-remote-access?ts=markdown)
  * [Web \& Phishing Security](https://www.paloaltonetworks.com/network-security/advanced-url-filtering?ts=markdown)  
    Cloud Security
  * [Application Security Posture Management (ASPM)](https://www.paloaltonetworks.com/cortex/cloud/application-security-posture-management?ts=markdown)
  * [Software Supply Chain Security](https://www.paloaltonetworks.com/cortex/cloud/software-supply-chain-security?ts=markdown)
  * [Code Security](https://www.paloaltonetworks.com/cortex/cloud/code-security?ts=markdown)
  * [Cloud Security Posture Management (CSPM)](https://www.paloaltonetworks.com/cortex/cloud/cloud-security-posture-management?ts=markdown)
  * [Cloud Infrastructure Entitlement Management (CIEM)](https://www.paloaltonetworks.com/cortex/cloud/cloud-infrastructure-entitlement-management?ts=markdown)
  * [Data Security Posture Management (DSPM)](https://www.paloaltonetworks.com/cortex/cloud/data-security-posture-management?ts=markdown)
  * [AI Security Posture Management (AI-SPM)](https://www.paloaltonetworks.com/cortex/cloud/ai-security-posture-management?ts=markdown)
  * [Cloud Detection \& Response](https://www.paloaltonetworks.com/cortex/cloud-detection-and-response?ts=markdown)
  * [Cloud Workload Protection (CWP)](https://www.paloaltonetworks.com/cortex/cloud/cloud-workload-protection?ts=markdown)
  * [Web Application \& API Security (WAAS)](https://www.paloaltonetworks.com/cortex/cloud/web-app-api-security?ts=markdown)  
    Security Operations
  * [Cloud Detection \& Response](https://www.paloaltonetworks.com/cortex/cloud-detection-and-response?ts=markdown)
  * [Security Information and Event Management](https://www.paloaltonetworks.com/cortex/modernize-siem?ts=markdown)
  * [Network Security Automation](https://www.paloaltonetworks.com/cortex/network-security-automation?ts=markdown)
  * [Incident Case Management](https://www.paloaltonetworks.com/cortex/incident-case-management?ts=markdown)
  * [SOC Automation](https://www.paloaltonetworks.com/cortex/security-operations-automation?ts=markdown)
  * [Threat Intel Management](https://www.paloaltonetworks.com/cortex/threat-intel-management?ts=markdown)
  * [Managed Detection \& Response](https://www.paloaltonetworks.com/cortex/managed-detection-and-response?ts=markdown)
  * [Attack Surface Management](https://www.paloaltonetworks.com/cortex/cortex-xpanse/attack-surface-management?ts=markdown)
  * [Compliance Management](https://www.paloaltonetworks.com/cortex/cortex-xpanse/compliance-management?ts=markdown)
  * [Internet Operations Management](https://www.paloaltonetworks.com/cortex/cortex-xpanse/internet-operations-management?ts=markdown)
  * [Extended Data Lake (XDL)](https://www.paloaltonetworks.com/cortex/cortex-xdl?ts=markdown)
  * [Agentic Assistant](https://www.paloaltonetworks.com/cortex/cortex-agentic-assistant?ts=markdown)  
    Endpoint Security
  * [Endpoint Protection](https://www.paloaltonetworks.com/cortex/endpoint-protection?ts=markdown)
  * [Extended Detection \& Response](https://www.paloaltonetworks.com/cortex/detection-and-response?ts=markdown)
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  * [Small \& Medium Business Solutions](https://www.paloaltonetworks.com/industry/small-medium-business-portfolio?ts=markdown)

* Services  
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  [Threat Intel and Incident Response Services](https://www.paloaltonetworks.com/unit42?ts=markdown)
  
  * [Assess](https://www.paloaltonetworks.com/unit42/assess?ts=markdown)
  
  * [AI Security Assessment](https://www.paloaltonetworks.com/unit42/assess/ai-security-assessment?ts=markdown)
  
  * [Attack Surface Assessment](https://www.paloaltonetworks.com/unit42/assess/attack-surface-assessment?ts=markdown)
  
  * [Breach Readiness Review](https://www.paloaltonetworks.com/unit42/assess/breach-readiness-review?ts=markdown)
  
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  * [Cloud Security Assessment](https://www.paloaltonetworks.com/unit42/assess/cloud-security-assessment?ts=markdown)
  
  * [Compromise Assessment](https://www.paloaltonetworks.com/unit42/assess/compromise-assessment?ts=markdown)
  
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  * [M\&A Cyber Due Diligence](https://www.paloaltonetworks.com/unit42/assess/mergers-acquisitions-cyber-due-diligence?ts=markdown)
  
  * [Penetration Testing](https://www.paloaltonetworks.com/unit42/assess/penetration-testing?ts=markdown)
  
  * [Purple Team Exercises](https://www.paloaltonetworks.com/unit42/assess/purple-teaming?ts=markdown)
  
  * [Ransomware Readiness Assessment](https://www.paloaltonetworks.com/unit42/assess/ransomware-readiness-assessment?ts=markdown)
  
  * [SOC Assessment](https://www.paloaltonetworks.com/unit42/assess/soc-assessment?ts=markdown)
  
  * [Supply Chain Risk Assessment](https://www.paloaltonetworks.com/unit42/assess/supply-chain-risk-assessment?ts=markdown)
  
  * [Tabletop Exercises](https://www.paloaltonetworks.com/unit42/assess/tabletop-exercise?ts=markdown)
  
  * [Unit 42 Retainer](https://www.paloaltonetworks.com/unit42/retainer?ts=markdown)
  
  * [Respond](https://www.paloaltonetworks.com/unit42/respond?ts=markdown)
  
  * [Cloud Incident Response](https://www.paloaltonetworks.com/unit42/respond/cloud-incident-response?ts=markdown)
  
  * [Digital Forensics](https://www.paloaltonetworks.com/unit42/respond/digital-forensics?ts=markdown)
  
  * [Incident Response](https://www.paloaltonetworks.com/unit42/respond/incident-response?ts=markdown)
  
  * [Managed Detection and Response](https://www.paloaltonetworks.com/unit42/respond/managed-detection-response?ts=markdown)
  
  * [Managed Threat Hunting](https://www.paloaltonetworks.com/unit42/respond/managed-threat-hunting?ts=markdown)
  
  * [Managed XSIAM](https://www.paloaltonetworks.com/cortex/managed-xsiam?ts=markdown)
  
  * [Unit 42 Retainer](https://www.paloaltonetworks.com/unit42/retainer?ts=markdown)
  
  * [Transform](https://www.paloaltonetworks.com/unit42/transform?ts=markdown)
  
  * [IR Plan Development and Review](https://www.paloaltonetworks.com/unit42/transform/incident-response-plan-development-review?ts=markdown)
  
  * [Security Program Design](https://www.paloaltonetworks.com/unit42/transform/security-program-design?ts=markdown)
  
  * [Virtual CISO](https://www.paloaltonetworks.com/unit42/transform/vciso?ts=markdown)
  
  * [Zero Trust Advisory](https://www.paloaltonetworks.com/unit42/transform/zero-trust-advisory?ts=markdown)  
    [Global Customer Services](https://www.paloaltonetworks.com/services?ts=markdown)
  
  * [Education \& Training](https://www.paloaltonetworks.com/services/education?ts=markdown)
  
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    UNIT 42 RETAINER
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* Partners  
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  * [Solution Providers](https://www.paloaltonetworks.com/partners/nextwave-solution-providers?ts=markdown)
  * [Managed Security Service Providers](https://www.paloaltonetworks.com/partners/managed-security-service-providers?ts=markdown)
  * [XMDR Partners](https://www.paloaltonetworks.com/partners/managed-security-service-providers/xmdr?ts=markdown)  
    Take Action
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  * [Become a Partner](https://paloaltonetworks.my.site.com/NextWavePartnerProgram/s/partnerregistration?type=becomepartner)
  * [Request Access](https://paloaltonetworks.my.site.com/NextWavePartnerProgram/s/partnerregistration?type=requestaccess)
  * [Find a Partner](https://paloaltonetworks.my.site.com/NextWavePartnerProgram/s/partnerlocator)  
    [CYBERFORCE
    CYBERFORCE represents the top 1% of partner engineers trusted for their security expertise.
    Learn more](https://www.paloaltonetworks.com/cyberforce?ts=markdown)

* Company  
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  Palo Alto Networks
  
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  * [Precision AI Security](https://www.paloaltonetworks.com/precision-ai-security?ts=markdown)
  * [Our Platform Approach](https://www.paloaltonetworks.com/why-paloaltonetworks/platformization?ts=markdown)
  * [Accelerate Your Cybersecurity Transformation](https://www.paloaltonetworks.com/why-paloaltonetworks/nam-cxo-portfolio?ts=markdown)
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    Careers
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    Read more](https://www.paloaltonetworks.com/company/press/2021/palo-alto-networks-secures-top-ranking-on-newsweek-s-most-loved-workplaces-list-for-2021?ts=markdown)

* More  
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  * [Threat Vector Podcast](https://unit42.paloaltonetworks.com/unit-42-threat-vector-podcast/)
  * [Packet Pushers Podcasts](https://www.paloaltonetworks.com/podcasts/packet-pusher?ts=markdown)  
    Connect
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1. [Cyberpedia](https://www.paloaltonetworks.com/cyberpedia?ts=markdown)
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3. [What Is Shadow AI? How It Happens and What to Do About It](https://www.paloaltonetworks.com/cyberpedia/what-is-shadow-ai?ts=markdown)  
   Table of contents

* [What is the difference between shadow IT and shadow AI?](#what-is-the-difference-between-shadow-it-and-shadow-ai)
* [How does shadow AI happen?](#how-does-shadow-ai-happen)
* [What are some examples of shadow AI?](#what-are-some-examples-of-shadow-ai)
* [What are the primary risks of shadow AI?](#what-are-the-primary-risks-of-shadow-ai)
* [How to determine how and when employees are allowed to use GenAI apps](#how-to-determine-how-and-when)
* [How to protect against shadow AI in 5 steps](#how-to-protect-against-shadow-ai-in-5-steps)
* [Top 5 myths and misconceptions about shadow AI](#top-5-myths-and-misconceptions-about-shadow-ai)
* [Shadow AI FAQs](#shadow-ai-faqs)

# What Is Shadow AI? How It Happens and What to Do About It

5 min. read  
Table of contents

* [What is the difference between shadow IT and shadow AI?](#what-is-the-difference-between-shadow-it-and-shadow-ai)
* [How does shadow AI happen?](#how-does-shadow-ai-happen)
* [What are some examples of shadow AI?](#what-are-some-examples-of-shadow-ai)
* [What are the primary risks of shadow AI?](#what-are-the-primary-risks-of-shadow-ai)
* [How to determine how and when employees are allowed to use GenAI apps](#how-to-determine-how-and-when)
* [How to protect against shadow AI in 5 steps](#how-to-protect-against-shadow-ai-in-5-steps)
* [Top 5 myths and misconceptions about shadow AI](#top-5-myths-and-misconceptions-about-shadow-ai)
* [Shadow AI FAQs](#shadow-ai-faqs)

1. What is the difference between shadow IT and shadow AI?

* [1. What is the difference between shadow IT and shadow AI?](#what-is-the-difference-between-shadow-it-and-shadow-ai)
* [2. How does shadow AI happen?](#how-does-shadow-ai-happen)
* [3. What are some examples of shadow AI?](#what-are-some-examples-of-shadow-ai)
* [4. What are the primary risks of shadow AI?](#what-are-the-primary-risks-of-shadow-ai)
* [5. How to determine how and when employees are allowed to use GenAI apps](#how-to-determine-how-and-when)
* [6. How to protect against shadow AI in 5 steps](#how-to-protect-against-shadow-ai-in-5-steps)
* [7. Top 5 myths and misconceptions about shadow AI](#top-5-myths-and-misconceptions-about-shadow-ai)
* [8. Shadow AI FAQs](#shadow-ai-faqs)

Shadow AI is the use of artificial intelligence tools or systems without the approval, monitoring, or involvement of an organization's IT or security teams.

It often occurs when employees use AI applications for work-related tasks without the knowledge of their employers, leading to potential security risks, data leaks, and compliance issues.

## What is the difference between shadow IT and shadow AI?

**Shadow IT refers to any technology---apps, tools, or services---used without approval from an organization's IT department.** Employees often turn to shadow IT when sanctioned tools feel too slow, limited, or unavailable.

As noted earlier, **Shadow AI is a newer, more specific trend. It involves using [artificial intelligence](https://www.paloaltonetworks.com/cyberpedia/artificial-intelligence-ai) tools---like ChatGPT or Claude---without formal oversight.** While it shares the same unofficial nature as shadow IT, shadow AI introduces unique risks tied to how AI models handle data, generate outputs, and influence decisions.  
"The thirst for AI capability is already resulting in shadow AI just like shadow IT was the first move toward cloud and software-as-a-service (SaaS) transformations. Security leaders will need to navigate that process again."  
[- Palo Alto Networks, ​​The Unit 42 Threat Frontier: Prepare for Emerging AI Risks](https://www.paloaltonetworks.com/resources/ebooks/unit42-threat-frontier)

The distinction matters. Shadow IT is mostly about unsanctioned access or infrastructure. Shadow AI is a [GenAI security risk](https://www.paloaltonetworks.com/cyberpedia/generative-ai-security-risks) focused on unauthorized use of AI tools, which can directly impact security, compliance, and business outcomes in more unpredictable ways.

## How does shadow AI happen?

Shadow AI happens when employees adopt generative AI tools on their own---without IT oversight or approval.

That might include using public tools like ChatGPT to summarize documents or relying on third-party AI plug-ins in workflows like design, development, or marketing.

In most cases, the intent isn't malicious. It's about getting work done faster. But because these tools fall outside sanctioned channels, they aren't covered by enterprise security, [governance](https://www.paloaltonetworks.com/cyberpedia/ai-governance), or compliance controls.  
![Diagram labeled 'Shadow AI' showing the flow of code and AI use within a corporate network. On the left, a dashed icon labeled 'GitHub' is connected to a solid arrow labeled 'Push to production' pointing to a blue square labeled 'TGW' representing a transit gateway. From TGW, arrows extend into a larger box labeled 'Corporate network.' Inside this box, two icons labeled 'App1' and 'App2' are arranged horizontally, with arrows indicating data flow between them and the TGW. Below and to the right, a circular icon labeled 'Developers' connects via a dashed arrow to an icon in the top right labeled 'Unsanctioned LLM Gen AI.' An arrow from this AI icon points into the corporate network, indicating interaction with internal applications. Arrows also flow back from App1 and App2 to TGW, indicating bidirectional data movement.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/Top-GenAI_11-Shadow.png "Diagram labeled 'Shadow AI' showing the flow of code and AI use within a corporate network. On the left, a dashed icon labeled 'GitHub' is connected to a solid arrow labeled 'Push to production' pointing to a blue square labeled 'TGW' representing a transit gateway. From TGW, arrows extend into a larger box labeled 'Corporate network.' Inside this box, two icons labeled 'App1' and 'App2' are arranged horizontally, with arrows indicating data flow between them and the TGW. Below and to the right, a circular icon labeled 'Developers' connects via a dashed arrow to an icon in the top right labeled 'Unsanctioned LLM Gen AI.' An arrow from this AI icon points into the corporate network, indicating interaction with internal applications. Arrows also flow back from App1 and App2 to TGW, indicating bidirectional data movement.")

It often starts with small decisions.

An employee might paste sensitive data into a chatbot while drafting a report. A team might use an open-source [LLM](https://www.paloaltonetworks.com/cyberpedia/large-language-models-llm) API to build internal tools without notifying IT. Developers may embed GenAI features into apps or pipelines using services like Hugging Face or OpenRouter. Others might use personal accounts to log into SaaS apps that include embedded AI features.

These behaviors rarely go through procurement, security, or compliance review.

What makes this so common is how accessible AI tools have become. Many are free, browser-based, or built into existing platforms.  
[![The State of Generative AI Whitepaper cover](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/brand_state-of-generative-AI-report-cover.jpg)](https://www.paloaltonetworks.com/resources/research/state-of-genai-2025)

* GenAI traffic surged more than 890% in 2024.
* The ratio of GenAI transactions as a percentage of SaaS also increased from 1% to 2% on average.

And because most organizations are still forming their governance approach, employees often act before formal guidance exists. Especially when centralized IT teams are stretched thin.

Here's the problem.

These tools often touch [sensitive data](https://www.paloaltonetworks.com/cyberpedia/sensitive-data). When used informally, they can introduce risks like [data leakage](https://www.paloaltonetworks.com/cyberpedia/data-leak), regulatory violations, or exposure to malicious models.

And because they're unsanctioned, IT and security teams usually don't even know they're in use. Let alone have a way to monitor or restrict them.

## What are some examples of shadow AI?

Shadow AI appears in subtle ways that don't always register as security events. Until they do.  
[![The State of Generative AI Whitepaper cover](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/brand_state-of-generative-AI-report-cover.jpg)](https://www.paloaltonetworks.com/resources/research/state-of-genai-2025)  
"Without proper IT security oversight, unauthorized use of GenAI apps can ultimately lead to an inadequate security posture and sensitive data loss."

**A product manager uses Claude to summarize an internal strategy deck before sharing it with a vendor.** The deck includes unreleased timelines and partner names. No one reviews the output, and the prompt history remains on Anthropic's servers.

**A developer builds a small internal chatbot that interfaces with customer data.** They use OpenRouter to access a fast open-source LLM via API. The project never enters the security backlog because it doesn't require infrastructure changes.

**A marketing designer uses Canva's AI image tools to generate campaign visuals from brand copy.** The prompt contains product names, and the final files are exported and reused in web assets. The team assumes it's covered under the standard SaaS agreement, but never verifies it with procurement or legal.

Each example seems routine, even helpful. But the tools fall outside security's line of sight, and their behavior often goes untracked.

## What are the primary risks of shadow AI?

![A two-column graphic titled 'Primary risks of shadow AI' displaying seven red-orange square icons with white illustrations, each paired with a risk label. On the left, four icons are aligned vertically: a person and document icon labeled 'Unauthorized processing of sensitive data'; a clipboard with a checkmark labeled 'Regulatory noncompliance'; four arrows pointing outward labeled 'Expansion of the attack surface'; and a chart with a warning symbol labeled 'Lack of auditability \& accountability.' On the right, three more icons appear: a neural network with a coin labeled 'Model poisoning \& unvetted outputs'; a database icon labeled 'Data leakage'; and a question mark on a badge labeled 'Overprivileged or insecure third-party access.' The icons are evenly spaced and set against a light background with a vertical divider.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/Shadow-AI_1-Primary.png "A two-column graphic titled 'Primary risks of shadow AI' displaying seven red-orange square icons with white illustrations, each paired with a risk label. On the left, four icons are aligned vertically: a person and document icon labeled 'Unauthorized processing of sensitive data'; a clipboard with a checkmark labeled 'Regulatory noncompliance'; four arrows pointing outward labeled 'Expansion of the attack surface'; and a chart with a warning symbol labeled 'Lack of auditability & accountability.' On the right, three more icons appear: a neural network with a coin labeled 'Model poisoning & unvetted outputs'; a database icon labeled 'Data leakage'; and a question mark on a badge labeled 'Overprivileged or insecure third-party access.' The icons are evenly spaced and set against a light background with a vertical divider.")

Shadow AI introduces risk not just because of the tools being used, but because they operate outside of formal oversight.  
[![The State of Generative AI Whitepaper cover](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/brand_state-of-generative-AI-report-cover.jpg)](https://www.paloaltonetworks.com/resources/research/state-of-genai-2025)  
"Shadow AI creates blind spots where sensitive data might be leaked or even used to train AI models."

* Organizations saw on average 66 GenAI apps, with 10% classified as high risk.
* We observed an average 6.6 high-risk GenAI apps per company.
* GenAI-related DLP incidents increased more than 2.5X, now comprising 14% of all DLP incidents.

That means no monitoring, no enforcement, and no guarantee of compliance.

Here's what that leads to:

* **Unauthorized processing of sensitive data:** Employees may submit proprietary, regulated, or confidential data into external AI systems without realizing how or where that data is stored.
* **Regulatory noncompliance:** Shadow AI tools can bypass data handling requirements defined by laws like [GDPR](https://www.paloaltonetworks.com/cyberpedia/gdpr-compliance), [HIPAA](https://www.paloaltonetworks.com/cyberpedia/what-is-hipaa), or the DPDP Act. That opens the door to fines, investigations, or lawsuits.
* **Expansion of the attack surface:** These tools often introduce unsecured APIs, personal device access, or unmanaged integrations. Any of those can serve as an entry point for attackers.
* **Lack of auditability and accountability:** Outputs from shadow AI are usually not traceable. If something fails, there's no way to verify what data was used, how it was processed, or why a decision was made.
* **Model poisoning and unvetted outputs:** Employees may use external models that have been trained on corrupted data. That means the results can be biased, inaccurate, or even manipulated.
* **Data leakage:** Some tools store inputs or metadata on third-party servers. If an employee uses them to process customer information or internal code, that data may be exposed without anyone knowing.
* **Overprivileged or insecure third-party access:** Shadow AI systems may be granted broad permissions to speed up a task. But wide access without control is one of the fastest ways to lose visibility and open gaps.

That's the real issue with shadow AI. It's not just a rogue tool. It's an entire layer of activity that happens outside the systems built to protect the business.  
| ***Further reading:***

* [*What Is a Prompt Injection Attack? \[Examples \& Prevention\]*](https://www.paloaltonetworks.com/cyberpedia/what-is-a-prompt-injection-attack)
* [*What Is Adversarial AI?*](https://www.paloaltonetworks.com/cyberpedia/what-are-adversarial-attacks-on-AI-Machine-Learning)

![Icon of a network](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/icon-unit-42-assessment_1.svg)

## Assess GenAI risks across your environment. Learn about the Unit 42 AI Security Assessment for GenAI Protection.

[Learn more](https://www.paloaltonetworks.com/unit42/assess/ai-security-assessment)

## How to determine how and when employees are allowed to use GenAI apps

![A horizontal flowchart titled 'How to decide how / when employees can use GenAI apps' presents five numbered steps. Each step is marked with a colored diamond icon and a brief description beneath. Step 1, in gray, shows a waveform icon and says 'Start with visibility into existing usage.' Step 2, in purple, shows a database icon and says 'Define what types of data are off-limits.' Step 3, in teal, shows a neural network diagram and says 'Assess how each tool stores \& processes inputs.' Step 4, in blue, shows a checklist icon and says 'Set role- or function-based permissions.' Step 5, in violet, shows a document with magnifying glass icon and says 'Establish a process for requesting \& reviewing new tools.' All steps are connected by dashed arrows.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/Shadow-AI_2-How.png "A horizontal flowchart titled 'How to decide how / when employees can use GenAI apps' presents five numbered steps. Each step is marked with a colored diamond icon and a brief description beneath. Step 1, in gray, shows a waveform icon and says 'Start with visibility into existing usage.' Step 2, in purple, shows a database icon and says 'Define what types of data are off-limits.' Step 3, in teal, shows a neural network diagram and says 'Assess how each tool stores & processes inputs.' Step 4, in blue, shows a checklist icon and says 'Set role- or function-based permissions.' Step 5, in violet, shows a document with magnifying glass icon and says 'Establish a process for requesting & reviewing new tools.' All steps are connected by dashed arrows.")

Most organizations don't start with a formal GenAI policy. Instead, they react to demand. Or to risk.

But waiting until something breaks doesn't work. The better approach is to decide ahead of time how and when GenAI apps are allowed.  
[![The State of Generative AI Whitepaper cover](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/brand_state-of-generative-AI-report-cover.jpg)](https://www.paloaltonetworks.com/resources/research/state-of-genai-2025)  
"Monitoring AI interactions is no longer optional. It's critical for helping prevent shadow AI adoption, enforcing security policies, and enabling responsible AI use."

Here's how to break it down:

### 1. Start with visibility into existing usage

You can't set rules for what you don't know exists.

Shadow AI often starts with personal accounts, browser plug-ins, or app features that don't get flagged by traditional tooling. That's why the first step is discovering what's already in use.

Tools like endpoint logs, SaaS discovery platforms, or browser extension audits can help.  
***Tip:***  
*Flag tools that evolve with AI features over time. Some approved SaaS apps quietly roll out GenAI capabilities without clear change logs or IT notifications. Add a line item to your risk review that periodically reassesses apps that integrate AI after approval. These quiet rollouts are a common backdoor for shadow AI.*

### 2. Define what types of data are off-limits

Not all data can be used with GenAI tools. Especially when the tools operate outside IT control.

Decide what categories of data should never be input, like customer records, source code, or regulated [PII](https://www.paloaltonetworks.com/cyberpedia/pii). That baseline helps set clear boundaries for all future decisions.  
***Tip:***  
*Tie data restrictions to real workflows. It's one thing to say "don't input customer data." But it's more effective to map those rules to actual team tasks. For example: Flag 'uploading CRM exports into GenAI tools' as a violation. People follow guidance better when it fits their actual workflow.*

### 3. Assess how each tool stores and processes inputs

Each GenAI app handles data differently. Some store prompts. Others train on user input. And some keep nothing at all.

Review the vendor's documentation. Ask how long data is retained, whether it's shared with third parties, and if it's used for model improvement.

### 4. Set role- or function-based permissions

Not every team needs the same level of access.

Developers might need API-based tools for prototyping. Marketing teams might only need basic writing support.

Designate who can use what. And for what purpose. That makes enforcement easier and reduces policy friction.

### 5. Establish a process for requesting and reviewing new tools

Shadow AI isn't just about what's already in use. It's also about what comes next.

Employees will continue finding new apps.

Instead of blocking everything, create a lightweight review process. That gives teams a path to get tools approved. And helps security teams stay ahead.

***Tip:***  
*Set a time-bound review cycle for approved tools. Policies shouldn't be "set it and forget it." Tools evolve fast, so make AI review approvals time-limited. Reassess every 6--12 months to confirm the tool's behavior hasn't changed and it's still safe to use.*

## How to protect against shadow AI in 5 steps

![A vertical two-column infographic titled 'How to protect against shadow AI' presents five labeled steps. The left column includes a vertical line connecting three circular icons and orange text for steps 1 through 3. Step 1 shows a wrench icon and reads 'Start with visibility across tools and usage.' Step 2 has an 'X' icon and reads 'Avoid blanket bans that drive usage underground.' Step 3 features an umbrella icon and reads 'Apply lessons from shadow IT governance.' The right column continues the line with two more icons and steps. Step 4, in orange, includes a checklist icon and reads 'Establish role- and function-based allowances.' Step 5, at the top right, features a document icon and reads 'Create a structured intake and review process.' Arrow graphics at the top and bottom frame the path of the flow.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/Shadow-AI_3-How.png "A vertical two-column infographic titled 'How to protect against shadow AI' presents five labeled steps. The left column includes a vertical line connecting three circular icons and orange text for steps 1 through 3. Step 1 shows a wrench icon and reads 'Start with visibility across tools and usage.' Step 2 has an 'X' icon and reads 'Avoid blanket bans that drive usage underground.' Step 3 features an umbrella icon and reads 'Apply lessons from shadow IT governance.' The right column continues the line with two more icons and steps. Step 4, in orange, includes a checklist icon and reads 'Establish role- and function-based allowances.' Step 5, at the top right, features a document icon and reads 'Create a structured intake and review process.' Arrow graphics at the top and bottom frame the path of the flow.")

Protecting against shadow AI means more than blocking tools. It means learning from what worked---and didn't---during the era of shadow IT.

Tools will always outpace policy. So the real focus needs to be on visibility, structure, and accountability.  
[![The State of Generative AI Whitepaper cover](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/brand_state-of-generative-AI-report-cover.jpg)](https://www.paloaltonetworks.com/resources/research/state-of-genai-2025)  
"Enterprises must implement robust safeguards around GenAI app classification, user access controls, and AI-specific DLP capabilities."

Here's how to approach it:

### Step 1: Start with visibility across tools and usage

Most organizations discover shadow AI use only after something goes wrong. But it's possible to get ahead of it.

Start with SaaS discovery tools, browser extension logs, and endpoint data.

Look for prompts sent to public LLMs, API connections to external models, and use of AI features in sanctioned apps. This helps establish a baseline. And gives security teams a place to start.  
***Tip:***  
*Visibility isn't just about identifying tools. It's about usage patterns. Add tagging or metadata to differentiate AI-powered features from the rest of an app. That helps distinguish between sanctioned use and unapproved GenAI capabilities embedded in trusted platforms.*

### Step 2: Avoid blanket bans that drive usage underground

Some companies try to eliminate risk by banning all GenAI use. But that often backfires.

Employees will still use these tools---just in less visible ways. That means less oversight, not more.

Instead, focus on enabling safe usage. That includes clear rules about what kinds of data can be used, where tools are allowed, and what needs approval first.  
***Tip:***  
*Use education as a deterrent. Instead of just blocking tools, share examples of real-world risks from unsanctioned GenAI use in internal security newsletters, onboarding materials, or team briefings. Tangible stories resonate more than abstract policies. And help reduce shadow adoption through awareness.*

### Step 3: Apply lessons from shadow IT governance

Shadow IT showed that enforcement-only strategies don't work. What did work was creating lightweight approval processes, offering secure internal alternatives, and giving teams space to innovate without bypassing oversight.

Shadow AI benefits from the same model. Think: enablement plus guardrails. Not lockdowns.

### Step 4: Establish role- and function-based allowances

One-size-fits-all policies tend to break. Instead, define GenAI permissions based on role, team function, or use case.

**For example(s):** Design teams might be cleared to use image generation tools under specific conditions. Developers might be allowed to use local LLMs for prototyping, but not customer data processing.

This keeps policy realistic and enforceable.

### Step 5: Create a structured intake and review process

Employees will keep finding new tools. That's not the problem.

The problem is when there's no way to flag or evaluate them. Offer a simple, well-defined process for requesting GenAI tool reviews.

This doesn't have to be a full risk assessment. Just enough structure to capture usage, evaluate risk, and decide whether to approve, restrict, or [sandbox](https://www.paloaltonetworks.com/cyberpedia/sandboxing).  
***Tip:***  
*Don't wait for employees to ask. Create a simple intake form and promote it internally. If users know there's an official path for approval, they're far less likely to go around it.*  
| ***Further reading:***

* [*What Is AI Prompt Security? Secure Prompt Engineering Guide*](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-prompt-security)
* [*How to Build a Generative AI Security Policy*](https://www.paloaltonetworks.com/cyberpedia/ai-security-policy)
* [*DSPM for AI: Navigating Data and AI Compliance Regulations*](https://www.paloaltonetworks.com/cyberpedia/dspm-data-ai-compliance)

![Icon of a network](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/icon-network-web.svg)

## Want to see how to gain visibility and control of all GenAI apps? Take the AI Access Security interactive tour.

[Launch tour](https://start.paloaltonetworks.com/ai-access-demo.html)

## Top 5 myths and misconceptions about shadow AI

![Graphical diagram of a shadow ai myths versus reality.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/shadow-ai-myths.png)

Shadow AI is a fast-moving topic, and it's easy to make assumptions. Especially when the tools feel familiar or harmless.

But not all common beliefs hold up. And misunderstanding the problem often leads to poor decisions around policy, risk, or enforcement.  
[![The State of Generative AI Whitepaper cover](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/what-is-shadow-ai/brand_state-of-generative-AI-report-cover.jpg)](https://www.paloaltonetworks.com/resources/research/state-of-genai-2025)  
"Your organization must adopt a proactive, multilayered approach to GenAI governance to effectively help mitigate AI risks."

Here are five myths worth clearing up:

1. **Myth #1: Shadow AI only means unauthorized tools**
   
   **Reality:** Not always. Shadow AI includes any AI use that lacks IT oversight. Even if the tool itself is technically allowed.
   
   **For example:** Using new GenAI features in an approved SaaS platform without going through an updated security review.

2. **Myth #2: Banning AI tools stops shadow AI**
   
   **Reality:** It rarely works that way. Blocking public GenAI apps can actually push users toward more obscure or unmanaged alternatives.
   
   That makes usage harder to track. And the risks are even harder to contain.

3. **Myth #3: Shadow AI is always risky or malicious**
   
   **Reality:** Most shadow AI use starts from good intentions. Employees are trying to save time or be more productive.
   
   The issue isn't motivation. It's that these actions bypass the normal review and approval process.

4. **Myth #4: Shadow AI is easy to detect**
   
   **Reality:** Not necessarily. Employees might use AI plug-ins inside approved tools, or access GenAI features from personal accounts.
   
   Without specific monitoring tools in place, a lot of shadow AI activity flies under the radar.

5. **Myth #5: Shadow AI only matters in technical roles**
   
   **Reality:** Wrong. Shadow AI shows up in marketing, HR, design, operations. Any team trying to move fast or experiment.
   
   And because these roles may not be security-focused, they're more likely to miss the risks.

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## Shadow AI FAQs

### What is the use of shadow AI?

Shadow AI is used when employees adopt AI tools outside of IT oversight to complete tasks like drafting content, analyzing data, or automating workflows---often to boost productivity. These tools operate without formal approval, which introduces governance and security risks.

### How can shadow AI be avoided?

Avoiding shadow AI starts with visibility, clear data handling rules, and defined approval paths. Instead of banning tools outright, organizations should offer sanctioned options and lightweight intake processes to guide safe usage.

### How can an organization detect shadow AI?

Detecting shadow AI requires monitoring endpoints, browser extensions, and SaaS activity. Discovery tools can surface unsanctioned GenAI use, such as prompt traffic, API calls to external models, or AI plug-ins embedded in approved software.

### What is Shadow GPT?

Shadow GPT typically refers to the unauthorized use of models like ChatGPT in workplace settings. It falls under shadow AI when employees use it without IT approval or process sensitive data in unmonitored ways.

### How do I delete Shadow AI?

Shadow AI isn't a product to uninstall. It refers to unmanaged AI use. Reducing it means identifying unsanctioned tools, applying governance controls, and aligning employees with approved AI workflows and policies.

### What is an example of shadow AI?

A developer using OpenRouter to build a chatbot that processes customer data---without notifying IT---is shadow AI. So is a marketing team using GenAI features in a SaaS app that hasn't been re-evaluated for AI risks.
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