What Is Shadow AI?

What is shadow AI?#

Shadow AI is the use of artificial intelligence tools, services, models, or features by employees without formal organizational approval or oversight. It includes public chatbots, coding assistants, meeting transcription services, browser extensions, and AI features embedded in existing software.

The term does not necessarily describe malicious activity. Most shadow AI begins when employees try to work faster, automate repetitive tasks, or solve a business problem before an approved solution is available.

Analyst’s Take: Start by finding out what employees are using and why. Discovery exposes the tools and data flows that need review, while an approved AI catalog gives employees a safer alternative to unmanaged services.

How shadow AI works#

An employee identifies an AI-enabled service and uses it with a personal account, an unapproved business account, or an existing corporate identity. They may paste a document into a chatbot, upload a spreadsheet for summarization, connect an AI assistant to a code repository, or enable an AI feature inside a collaboration platform.

The organization may have little or no visibility into:

• What data was submitted
• Which model or provider processed it
• Whether the provider retains prompts
• Who can access generated content
• Whether the service uses data for training
• Which integrations or plugins are connected
• How long the data remains available

Shadow AI can also occur without a new application. An employee might activate an AI capability in an approved SaaS platform, install an AI browser extension, use a local model on a workstation, or call an AI API from a script. The application itself may be legitimate, while the use case, data, or configuration is not governed.

The security risk depends on the combination of tool, data, identity, and workflow. Using a public chatbot to rewrite a generic paragraph is materially different from submitting customer records, source code, regulated data, credentials, or internal incident details.

Technical notes: Signals that reveal shadow AI

No single control identifies all shadow AI. Use several telemetry sources and correlate them:

Identity:   New OAuth grants, personal accounts, unfamiliar SSO applications
Network:    DNS and proxy requests to AI domains, API endpoints, upload activity
SaaS:       Newly enabled AI features, external sharing, connected integrations
Endpoint:   Browser extensions, desktop clients, local model runtimes
Data:       DLP matches, unusual copy-and-paste, large uploads, sensitive file access
Finance:    New subscriptions, recurring card charges, software reimbursements
Code:       AI API keys, new dependencies, model downloads, repository integrations

A basic starting point is a DNS or proxy search for known AI-related domains. Treat the result as an indicator, not proof of a policy violation:

# Example log search pattern; adapt field names to your platform
destination_domain contains_any (
  "chatgpt",
  "claude",
  "gemini",
  "copilot",
  "perplexity"
)
| stats count(), dcount(user), sum(bytes_uploaded)
  by destination_domain, user
| sort sum(bytes_uploaded) desc

Domain lists become outdated quickly, and many providers use shared infrastructure. Supplement them with application discovery, browser telemetry, SaaS audit logs, and API traffic analysis. Review findings with privacy and legal teams when monitoring could expose personal activity or sensitive employee information.

When you’ll encounter shadow AI#

Shadow AI commonly appears when employees face one of four conditions:

1. A productivity gap: Teams need summarization, translation, drafting, transcription, analysis, or automation immediately.
2. Slow procurement: The approved software process takes longer than the business problem can wait.
3. Unclear policy: Employees do not know which AI tools are permitted or what information they may submit.
4. Embedded functionality: An approved product adds an AI feature that users enable without a separate security review.

Common examples include:

• Marketing staff generating campaign copy with a public chatbot
• Developers using an AI coding assistant with private repositories
• Support teams pasting customer tickets into a summarization tool
• Recruiters uploading resumes to an AI screening service
• Finance teams using AI to analyze spreadsheets
• Employees enabling meeting transcription or note-taking bots
• Analysts downloading models or using AI APIs in notebooks
• Staff connecting personal automation tools to corporate SaaS accounts

The highest-risk cases usually involve sensitive data, privileged access, regulated workflows, external sharing, or automated decisions. A tool used by one employee is still worth recording. A connected AI service with access to email, repositories, customer systems, or shared drives deserves faster review.

How to find shadow AI in your organization#

Start with an inventory rather than a blocklist. Ask business and technical teams which AI tools they use, what tasks those tools support, and what information they process. Anonymous surveys can reveal tools that technical telemetry misses. Direct interviews explain why employees adopted them.

Then compare that information with technical evidence:

• Review IdP and SSO application catalogs for unknown AI services and OAuth grants.
• Query secure web gateway, DNS, firewall, and proxy logs for AI domains and API endpoints.
• Use CASB, SaaS security, or cloud access discovery to identify unsanctioned applications.
• Inspect endpoint management data for AI desktop apps, browser extensions, and local runtimes.
• Review DLP alerts involving uploads, copy-and-paste, source code, credentials, and personal data.
• Search expense reports, procurement records, and corporate card transactions for AI subscriptions.
• Examine repository settings for AI integrations, exposed API keys, and unexpected automation.
• Check audit logs for newly enabled AI features in approved SaaS products.

Create a register with the tool name, owner, users, business purpose, data types, integrations, provider terms, and risk rating. Validate each finding before taking action. A domain visit does not establish what data was entered, and an AI label does not automatically make a service unsafe.

How AI governance helps manage shadow AI#

AI governance provides the policies, ownership, review processes, and technical safeguards needed to manage AI use consistently. Organizations building a broader control framework can compare their approach with the NIST Cybersecurity Framework glossary, while recognizing that AI governance also requires data-use, privacy, and model-specific considerations.

A practical governance program should define:

• Which AI tools are approved
• Which information employees may submit
• What account and authentication requirements apply
• How providers handle retention and model training
• Which use cases require human review
• Who owns each AI service and workflow
• How employees report new tools or risky behavior
• How exceptions are reviewed and documented

An approved AI catalog is especially important. Employees are more likely to follow policy when they have a fast, practical alternative to an unmanaged service.

What to do after discovering shadow AI#

Publish a simple acceptable-use policy that defines approved tools, prohibited data, account requirements, retention expectations, and escalation paths. Provide an approved AI catalog so employees have a practical alternative to unsafe services.

For higher-risk use cases, require data classification, vendor review, access controls, logging, retention settings, human oversight, and testing for inaccurate or harmful outputs. Use conditional access, browser controls, DLP, or proxy restrictions for clearly prohibited activity, but do not rely on blocking alone. Employees may move to unmanaged devices or less visible services.

Strong identity hygiene also matters. Require multifactor authentication, review OAuth permissions, remove dormant accounts, and discourage personal accounts for business data. A password manager such as 1Password can help teams use unique credentials and manage access to approved services without sharing passwords.

For endpoints, monitor unauthorized extensions, desktop clients, local model runtimes, and unusual data transfers. Security software such as Malwarebytes may support endpoint protection, but it should complement—not replace—identity, SaaS, network, and data controls.

Review the inventory regularly. AI products, embedded features, and provider terms change quickly, so shadow AI discovery should become part of SaaS management, identity governance, data security, and third-party risk processes.

Related terms

• Shadow IT: Unapproved hardware, software, or cloud services used outside formal IT governance. Shadow AI is a specialized form involving AI capabilities.
• AI governance: Policies, controls, accountability, and oversight for developing or using AI.
• Generative AI: AI that produces text, code, images, audio, video, or other content from user instructions.
• Bring your own AI (BYOAI): Employees selecting and using AI tools independently of an organization’s approved technology stack.
• AI sprawl: The uncontrolled growth of AI tools, models, integrations, accounts, and use cases across an organization.
• SaaS discovery: The process of identifying cloud applications in use, including applications that were not formally procured.
• Data loss prevention (DLP): Controls that detect or restrict sensitive data movement, including uploads to AI services.

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