Shadow AI: What It Is, Risks, Examples, Detection & Prevention

1st November 2026

Shadow AI is becoming a growing security concern as employees use artificial intelligence (AI) tools to complete everyday tasks. These tools can help people write emails, summarize reports, analyze data, generate code, and automate repetitive work. However, problems can arise when employees use AI applications without appropriate approval or security oversight.

For example, an employee might upload a confidential financial report to a public AI chatbot to prepare a presentation. A developer might install an unapproved coding assistant that can access proprietary source code. Another employee might connect an AI agent to internal documents without reviewing its permissions.

These activities may seem harmless, but they can expose sensitive information or give AI applications access to systems they do not need.

For enterprise security teams, the challenge is not just finding unauthorized AI tools. They also need to understand what information these tools access, which identities and systems they connect to, and what actions they can perform.

Grafyn's AI Security Platform helps organizations understand their AI environment, discover hidden AI usage, assess sensitive data exposure, and examine how identities and connected systems influence potential security risks.

What Is Shadow AI?

Shadow AI refers to the use of AI tools, models, browser extensions, coding assistants, copilots, AI agents, and Model Context Protocol (MCP) servers without appropriate organizational approval, visibility, or oversight. These technologies may be adopted by employees or development teams faster than security teams can discover, review, and govern them.

For example, a developer may connect a custom MCP server to an internal database without authentication or security approval. This can expose enterprise data and create unauthorized access paths to internal systems. Grafyn discovers MCP servers across the environment and uses built-in security policies to flag cases such as MCP servers operating without authentication.

It can include employees using personal AI accounts for work, installing unapproved AI extensions, or connecting agents to business applications without a security review.

Shadow AI is not limited to public chatbots. It can also exist inside approved software when new AI features, integrations, or automated workflows have not been reviewed.

For example, a marketing employee may use an AI tool to summarize customer feedback. If the tool receives customer records and has not been approved for that data, the organization may not have sufficient control over how the information is processed.

The main problem is limited visibility and control. Security teams may not know which AI tools are being used, what data they receive, or what access they have. Grafyn's Discovery & Visibility capabilities focus on identifying AI assets and understanding their relationships with identities, data sources, tools, and connected systems. This context helps security teams investigate Shadow AI and assess where additional controls may be needed.

Why Is Shadow AI Becoming a Concern?

Employees can access AI tools through websites, desktop applications, browser extensions, developer tools, and third-party integrations. In many cases, they can start using these services without help from IT.

Several factors contribute to Shadow AI adoption:

Easy access: Many AI tools require only an account and an internet connection.
Productivity needs: Employees use AI to write content, analyze information, summarize documents, and generate code.
Slow approval processes: Employees may look for alternatives when approved tools are unavailable or difficult to access.
Limited visibility: Traditional software inventories may miss browser extensions, embedded AI features, and new agent integrations.
Expanding access: AI agents can connect to documents, databases, APIs, and business applications.
Unclear policies: Employees may not know which tools are approved or what information they can safely share.

Shadow AI does not always mean employees are deliberately ignoring security policies. Often, they are trying to work more efficiently. Organizations should therefore combine technical controls with clear policies, practical training, and accessible approved alternatives.

As AI adoption grows, organizations need visibility beyond their approved software lists. Grafyn's Shadow AI solution focuses on discovering AI usage across enterprise environments, helping security teams investigate applications and integrations that may otherwise go unnoticed.

What Are Common Examples of Shadow AI?

Shadow AI can emerge across departments when employees use unapproved AI tools, browser extensions, plug-ins, or services without the organization's knowledge or security review. These practices can expose sensitive information, create compliance risks, and reduce visibility into how business data is processed.

Common examples of Shadow AI vary by industry:

Shadow AI by Industry

Financial Services: Financial advisors and analysts may paste client portfolios, account statements, or other confidential financial information into unapproved AI chatbots to summarize data or prepare reports. This can expose sensitive customer information outside approved systems.

Healthcare: Clinicians may use unapproved AI scribes to transcribe consultations or paste patient notes into public AI tools to summarize records. Without appropriate safeguards, these practices can expose protected health information and create privacy and compliance risks.

Retail: Store employees and office staff may paste customer details, purchase histories, sales reports, or other business information into public AI tools to
generate summaries, analyze trends, or draft communications. Such activity can expose customer data and reduce organizational visibility into AI usage.

Software and Hi-Tech: Development and engineering teams may adopt AI coding assistants, external AI services, or plug-ins without a security review. Employees might inadvertently expose proprietary source code, credentials, technical documentation, or internal system details through these tools.

Other Common Forms of Shadow AI

Shadow AI extends beyond employees using unapproved public chatbots. It can also enter enterprise environments through connected agents, browser extensions, embedded AI features, and MCP servers.

AI Agents Connected to Business Systems: Employees or teams may deploy third-party AI agents that connect to CRM platforms, cloud storage, internal databases, or business applications without security approval. These agents may inherit access to sensitive data through connected user identities or service accounts, creating exposure and potential blast-radius risks if an agent is compromised or misused.
Unauthorized AI Browser Extensions: Employees may install AI-powered browser extensions to summarize webpages, draft emails, or analyze business content. Depending on their permissions, these extensions may access browser activity, page content, or information from authenticated enterprise applications.
AI Features Embedded in Approved Software: Employees may use built-in AI assistants or generative AI features within approved workplace applications without understanding what information is shared with the underlying AI service. An approved application does not automatically mean every AI feature or data-sharing configuration has been reviewed.
Unapproved MCP Servers: Developers or teams may connect external Model Context Protocol (MCP) servers to AI assistants to access databases, code repositories, APIs, or other enterprise tools. Without security review, these servers may introduce excessive permissions, untrusted tools, or unintended access to sensitive systems.

Grafyn's Shadow AI capabilities help organizations discover unsanctioned AI tools, browser extensions, agents, and MCP servers across endpoints, cloud environments, and code. By mapping AI assets and their connections, Grafyn helps security teams understand exposure paths, identify risky usage, and assess potential blast radius.

What are the Main Risks of Shadow AI?

The level of risk depends on the information involved, the permissions granted, the connected systems, and the actions the AI tool can perform.

1. Sensitive Data Exposure

Employees may share customer information, financial reports, contracts, source code, or internal research with unapproved AI services.

The consequences depend on the service's data handling practices, configuration, and contractual terms. Without visibility into the actual workflow, security teams may struggle to determine whether information was retained, shared, or handled according to company requirements.

Organizations should identify sensitive information, define where it can be processed, and apply appropriate safeguards. Grafyn's Sensitive Data Disclosure capabilities focus on understanding sensitive data exposure in AI environments, helping security teams investigate how AI assets relate to confidential information and connected resources.

2. Privacy and Compliance Risks

Organizations may have legal or contractual obligations governing personal information, financial records, healthcare data, and confidential business information.

For example, a healthcare organization must assess applicable requirements before allowing an AI tool to process protected health information. A financial services company may need to consider customer confidentiality and recordkeeping obligations.

Using AI does not automatically create a compliance violation. The risk depends on the data involved, applicable requirements, the service's terms, and the safeguards in place.

3. Excessive Permissions and Identity Risks

AI applications may operate through employee accounts, service accounts, API credentials, or delegated access. If an AI agent receives more permissions than it needs, a compromise or misuse could affect additional data and systems.

For example, an agent designed to summarize selected project documents may not need unrestricted access to company-wide cloud storage. Organizations should review identities, access permissions, credentials, and connections to business applications.

Grafyn's Agentic Identity Blast Radius Analysis helps organizations examine the relationships between AI agents, identities, permissions, and connected systems. This context can support reviews of excessive access and help teams understand which connections may increase potential incident impact.

4. Unauthorized Actions

Some AI agents can execute code, update records, send emails, or trigger automated workflows.

Incorrect instructions, malicious content, or poor configuration may lead to unintended actions, especially when agents have broad permissions and operate without human approval.

Organizations should limit available tools, validate sensitive actions, maintain logs, and require human approval for high-impact operations.

5. Intellectual Property Exposure

Uploading proprietary source code, product designs, business plans, or trade secrets to an unapproved AI service may create confidentiality concerns.

Organizations should define which information employees can share and evaluate the provider's data handling terms before approving a tool.

6. Limited Auditability

Unapproved AI usage may leave security teams without enough information to determine what data was shared, which identity was involved, or what actions occurred. This can make incident investigations and exposure assessments more difficult.

A clearer inventory of AI assets, identities, and connections can help security teams investigate incidents and identify areas requiring further review. Grafyn's Discovery & Visibility focuses on building visibility into AI assets and their relationships across connected environments. Actual investigation capabilities depend on available data and integrations.

7. Increased Incident Impact

The impact of an AI security incident depends partly on how far an affected application can reach.

An agent with access to one approved document has a different risk profile from an agent that can retrieve sensitive records, access multiple applications, and execute business actions. AI blast radius analysis examines which identity an agent operates under, what sensitive data it can access, which actions it can perform, and which trust boundaries it crosses to determine how far a compromise or misuse could spread. Grafyn's Agentic Identity Blast Radius Analysis and Blast Radius Detection help security teams understand these relationships, identify high-risk exposure paths, and prioritize risks for review.

What Is the Difference Between Shadow AI and Shadow IT?

Shadow AI is a specialized form of Shadow IT. Shadow IT refers to technology used without appropriate organizational approval or oversight. Shadow AI focuses on unauthorized or ungoverned AI tools, models, agents, and related services.

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Area	Shadow IT	Shadow AI
Main concern	Unapproved technology	Unapproved AI usage and access
Examples	Unauthorized cloud storage or SaaS tools	Public chatbots, coding assistants, AI agents
Data risks	Unapproved storage and transfers	Sensitive information shared with AI services
Access risks	Unmanaged accounts and permissions	AI tools using user or agent permissions
Additional concerns	Software misconfiguration	Automated actions and AI-specific risks
Key controls	IT discovery and access management	AI discovery, data-flow visibility, permission reviews, and governance

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Traditional IT security controls remain important. However, identifying an AI application is only the first step. Organizations must also understand its data access, identities, integrations, and potential impact.

How to Detect Shadow AI

Effective Shadow AI detection combines application discovery, identity analysis, data-flow visibility, and employee engagement.

1. Discover AI Applications

Review available information from:

Secure web gateways and DNS logs.
Endpoint and software inventories.
Browser extension inventories.
Identity provider sign-in logs.
Cloud application security tools.
API gateways and cloud audit logs.
Developer environments and software repositories.

These sources can help identify connections to known AI services. However, network activity alone may not show what information was shared or what actions an AI tool performed.

2. Identify AI Features in Existing Software

Review AI capabilities within productivity tools, CRM systems, coding platforms, analytics applications, and other business software.

An approved application may introduce AI features with separate data processing or permission requirements. Review those features before allowing them to process sensitive information.

3. Review OAuth Permissions and API Access

OAuth permissions determine what an application can access through an authorized connection. API credentials can also allow applications and agents to interact with business systems.

Check whether an AI tool can read documents, edit records, send emails, or access customer information. Remove unnecessary permissions and use narrowly scoped access wherever possible.

For example, an agent that only summarizes reports should not automatically have permission to modify financial records.

Reviewing permissions is particularly important when AI tools connect to enterprise applications. Grafyn's Agentic Identity Blast Radius Analysis provides a related security perspective by focusing on identity relationships, permissions, and the potential impact of connected access. Use your identity provider and application audit logs to verify the actual OAuth grants and API permissions.

4. Map Data Flows and Connections

Understand where AI tools obtain information, how it is processed, and which systems receive the output.

An AI assistant might retrieve internal documents, send selected content to an external model, and return a summary to an internal application.

Mapping these relationships helps identify unnecessary data transfers, sensitive information exposure, and connections that require additional review.

5. Monitor Changes and Unusual Activity

Monitor relevant environments for new AI applications, unexpected permission changes, sensitive data access, and new connections to external services.

The scope of monitoring will depend on the organization's infrastructure and available integrations. Employee privacy requirements should also be considered.

6. Engage Employees

Ask teams which AI tools they use, what tasks they perform, and what limitations they experience with approved alternatives.

Employee feedback can reveal tools that technical monitoring misses. It can also help organizations understand why people turn to unauthorized services.

7. Maintain a Central AI Inventory

Record each discovered AI asset's provider, purpose, owner, approval status, data access, connected systems, risk level, and review status. Update the inventory as new tools and integrations appear. This gives security teams a clearer basis for prioritizing assessments.

An AI inventory is more useful when it connects asset records with information about data access, identities, and integrations. Grafyn's AI Security Platform provides a broader approach to AI discovery and exposure assessment that can complement an organization's inventory and governance processes.

How to Prevent and Control Shadow AI

Preventing Shadow AI requires clear policies, secure alternatives, appropriate technical controls, and ongoing oversight.

1. Establish a Clear AI Usage Policy

Explain which AI tools employees can use and what information they may submit.

The policy should cover confidential data, customer records, AI-generated code, connected agents, human review requirements, and incident reporting.

Use practical examples to explain what is permitted and what requires approval.

2. Provide Approved AI Alternatives

Offer accessible, approved tools for common tasks such as writing, research, coding, and data analysis.

Employees are more likely to follow security requirements when approved tools meet their practical needs and the approval process is straightforward.

3. Classify AI Usage by Risk

Apply controls according to data sensitivity, permissions, integrations, and potential business impact.

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Risk level	Example	Recommended response
Lower	AI tool processing public information	Apply standard usage rules
Moderate	Approved AI tool processing internal information	Apply defined data and access controls
High	Unapproved tool receiving confidential information	Restrict use and investigate potential exposure
Critical	Agent with broad access and high-impact permissions	Restrict or isolate access until reviewed

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These categories are illustrative. Each organization should establish its own criteria based on its risk tolerance and obligations.

Risk classification should consider more than the name of an AI application. Its access to sensitive information, identity permissions, connected tools, and potential downstream impact can change its risk profile. Grafyn's Sensitive Data Disclosure and Blast Radius Detection capabilities are relevant to assessing these dimensions.

4. Apply Least-Privilege Access

Give each AI application and agent only the access required for its intended task.

Use scoped credentials, role-based access, separate service identities, and regular permission reviews. Revoke access that is no longer necessary.

Least-privilege access should be reviewed across users, service accounts, AI agents, and connected applications. Grafyn's Agentic Identity Blast Radius Analysis focuses on understanding how identity and access relationships affect the potential reach of an AI-related incident. Access changes should be implemented through the relevant identity and application controls.

5. Use Data Loss Prevention

Data loss prevention (DLP) tools can help identify or restrict certain transfers of sensitive information to unauthorized destinations. Depending on the product and integration, controls may cover web uploads, email, endpoints, cloud applications, or supported AI interactions.

Verify actual coverage rather than assuming every prompt, upload, or AI feature can be inspected.

6. Control AI Agent Actions

Restrict the tools and APIs available to agents. Limit access to specific records, log important actions, validate requests, and require human approval for sensitive or irreversible operations. These controls can reduce the potential impact of mistakes or misuse.

For additional assurance, organizations can evaluate how AI agents interact with tools, APIs, and downstream systems. Grafyn's Agentic Red Teaming focuses on adversarial testing of AI agents, their tool calls, permissions, and connected workflows. This can complement least-privilege access, action validation, and human approval controls.

7. Review AI Usage Regularly

Reassess AI assets when providers, permissions, integrations, or use cases change. Track new discoveries, high-risk findings, unnecessary permissions, and remediation progress to understand whether the program is improving.

What Tools Help Manage Shadow AI?

Shadow AI management usually requires several complementary security capabilities.

AI discovery and security platforms: Help identify AI assets and assess their relationships with identities, data, and connected systems.
Cloud application security: Helps discover cloud applications and enforce supported access policies.
Data loss prevention: Helps identify or restrict certain transfers of sensitive information.
Identity and access management: Supports authentication, permission reviews, and access restrictions.
Endpoint and network security: Helps identify installed applications, browser extensions, and connections to known AI services.
AI governance platforms: Support ownership, risk assessments, approvals, policies, and documentation.

Organizations can use AI-specific security platforms such as Grafyn alongside existing Cloud Access Security Broker (CASB), Data Loss Prevention (DLP), endpoint security, network security, and identity and access management (IAM) tools. While traditional security tools help enforce established controls, an AI-specific security layer can provide visibility into AI assets, agent identities, sensitive data exposure, and relationships that may expand an AI system's blast radius.

Grafyn can complement existing security infrastructure by helping organizations assess AI-specific risks across connected environments and prioritize potential exposure paths.

No single tool should be assumed to discover every AI asset or enforce every security control across all environments. Organizations should evaluate integrations, coverage, and verified capabilities against their specific security requirements.

How Grafyn Helps Manage Shadow AI

Grafyn helps organizations approach Shadow AI as more than an application discovery problem. Security teams need to understand what AI tools are being used, which data and identities they connect to, and how those relationships may affect enterprise security.

1. Discover Hidden AI Usage

Organizations need visibility into AI tools, browser extensions, coding assistants, copilots, agents, and connected workflows.

Grafyn's Shadow AI solution focuses on discovering AI usage across relevant enterprise environments. This can help security teams build a clearer inventory of AI activity that may not appear in traditional software inventories.

2. Understand AI Assets and Their Connections

Identifying an AI application does not explain its complete risk. Security teams also need to understand its relationships with data sources, identities, tools, and connected systems.

Grafyn's Discovery & Visibility offering focuses on these relationships, helping teams investigate how AI assets interact with the broader enterprise environment.

3. Investigate Sensitive Data Exposure

An AI tool may introduce additional risk when it can access confidential documents, customer records, source code, or other sensitive information.

Grafyn's Sensitive Data Disclosure capabilities focus on understanding sensitive data exposure in AI environments. This context can help security teams identify areas that require closer assessment and appropriate access controls.

4. Assess Identity Permissions and Potential Blast Radius

AI agents may use user accounts, service identities, delegated permissions, or connections to external tools. These relationships can influence how far a potential security incident could spread.

Grafyn's Agentic Identity Blast Radius Analysis and Blast Radius Detection focus on understanding and minimizing potential impact across connected systems.

5. Test AI Agent Security

Some AI agents can call tools, access APIs, and trigger actions in downstream applications. Organizations should evaluate whether these interactions behave as intended under adversarial conditions.

Grafyn's Agentic Red Teaming focuses on testing agent behavior, tool calls, permissions, and connected workflows to help teams understand potential attack paths and assess whether controls limit their impact.

6. Support AI Governance

Discovery and risk assessment should inform decisions about approval, ownership, access, and ongoing oversight.

Grafyn's AI Governance offering complements its security-focused capabilities with governance-related support. The specific enforcement and monitoring features available depend on product configuration and integrations.

Together, these capabilities support a more informed approach to Shadow AI: discover AI usage, investigate exposure, understand connected access, assess potential impact, and apply appropriate controls.

Explore the Grafyn AI Security Platform or schedule a demo to discuss your organization's AI security requirements.

How to Build a Shadow AI Management Program

Organizations can begin with five practical phases.

Phase 1: Discover. Identify AI applications, agents, integrations, owners, and potential data exposure.

Phase 2: Assess. Review permissions, sensitive data access, connected systems, and potential business impact.

Phase 3: Govern. Publish an AI usage policy, identify approved tools, and create an efficient approval process.

Phase 4: Control. Apply least-privilege access, supported DLP controls, monitoring, and appropriate restrictions on AI applications and agent actions.

Phase 5: Improve. Review new discoveries, investigate incidents, track remediation, and reassess controls as AI usage changes.

Useful performance indicators include the percentage of discovered assets with assigned owners, risk assessment coverage, time taken to address high-risk findings, unnecessary permissions removed, and adoption of approved tools.

Conclusion

Shadow AI becomes a security challenge when employees adopt AI tools faster than organizations can discover, assess, and govern them.

Managing the problem requires more than blocking unauthorized applications. Security teams need to understand where AI is used, what information it accesses, which identities and permissions it relies on, and what actions it can perform.

A practical Shadow AI program combines continuous discovery, risk assessment, clear policies, approved alternatives, least-privilege access, data protection, and ongoing monitoring.

Grafyn's approach focuses on discovering hidden AI usage, understanding its connections to enterprise data and systems, assessing potential exposure, and supporting governance.

The goal is not to eliminate AI from the workplace. It is to make AI adoption visible, secure, and accountable.

Want to understand your organization's AI exposure? Explore Grafyn's AI Security Platform or schedule a demo.

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Frequently Asked Questions

What is Shadow AI in simple terms?

Shadow AI is the use of AI tools at work without appropriate organizational approval or oversight. For example, an employee might upload a confidential report to an unapproved chatbot.

Is Shadow AI always dangerous?

No. The risk depends on the tool, information involved, permissions, and intended use. However, unknown AI usage makes it harder for organizations to assess and manage potential exposure.

What is the difference between Shadow AI and generative AI?

Generative AI creates content such as text, images, or code. Shadow AI describes AI usage that falls outside an organization's approval or oversight processes. A generative AI tool can become Shadow AI when used without appropriate authorization.

Can approved software still introduce Shadow AI risks?

Yes. An approved application may introduce new AI features, integrations, or agents that have not been reviewed separately.

How can companies detect Shadow AI?

Companies can combine application discovery, network monitoring, endpoint inventories, identity logs, cloud security tools, API monitoring, and employee surveys.

How can organizations prevent AI data leaks?

Use approved tools, clear data handling policies, least-privilege access, DLP where supported, employee training, and monitoring of relevant activity.

What is a Shadow AI agent?

A Shadow AI agent is an AI system that performs tasks or interacts with business systems without appropriate approval or oversight. Depending on its permissions, it may retrieve information, execute code, update records, or trigger workflows.

Does Shadow AI automatically create compliance violations?

No. Compliance depends on the information processed, applicable obligations, provider terms, and safeguards in place.

What should a company do after discovering Shadow AI?

Identify the tool's owner, data access, permissions, and integrations. Assess the risk, restrict unnecessary access, preserve relevant evidence, and investigate potential exposure. Provide an appropriate approved alternative where possible.

How does Grafyn help manage Shadow AI?

Grafyn discovers Shadow AI across endpoints, clouds, and code, including AI coding tools, browser extensions, agents, and MCP servers. It maps their connections to identities, sensitive data, and enterprise systems, assesses potential blast radius, and flags risky AI usage through policies. This helps security teams identify unsanctioned AI deployments, understand exposure paths, and prioritize security risks across their AI environments.