An employee might upload confidential documents to a personal ChatGPT<\/a> account, a developer might connect an unapproved AI model to a company application, or a department might purchase an AI subscription without involving IT. These actions may be intended to improve productivity, but they can introduce security, privacy, financial and compliance risks.<\/p>\n\n\n\n The challenge is not simply that employees are using AI. It is that organisations may have limited visibility into which tools are being used, what information they can access and how much they cost.<\/p>\n\n\n\n WrangleAI helps organisations improve visibility and control over AI usage<\/a> across supported providers, models, applications, API keys and agents. By bringing usage monitoring, cost management and governance controls together, it can help teams manage approved AI infrastructure and identify unusual activity within the environments they monitor.<\/p>\n\n\n\n However, managing Shadow AI requires more than a monitoring platform. Organisations also need clear policies, employee education, identity controls, vendor reviews and ways to discover AI activity outside their approved systems.<\/p>\n\n\n\n This guide explains what Shadow AI is, why it creates risks, how organisations can detect unapproved AI tool usage and which controls can help businesses adopt AI more safely.<\/p>\n\n\n Shadow AI refers to artificial intelligence tools, models, applications or services used within an organisation without the knowledge, approval or oversight of the teams responsible for IT, security or AI governance.<\/p>\n\n\n\n It is closely related to shadow IT, where employees use technology outside official company processes. However, Shadow AI introduces additional risks because AI tools may process sensitive information, generate business decisions, connect with internal systems or take actions through automated agents.<\/p>\n\n\n\n Shadow AI can appear in several forms.<\/p>\n\n\n\n An employee may use a personal AI account to summarise confidential meeting notes. A developer may integrate an external language model into a production application without completing a security review. A sales team may adopt an AI-powered customer data tool without checking its privacy terms.<\/p>\n\n\n\n In other cases, employees may use AI features built into software that the organisation has already approved, without realising that those features send information to additional AI services.<\/p>\n\n\n\n Not every unapproved AI tool is necessarily unsafe. The problem is that the organisation has not assessed its risks, approved its intended use or established appropriate controls.<\/p>\n\n\n\n Shadow AI often develops because employees find useful tools faster than organisations can review and approve them.<\/p>\n\n\n\n AI products are widely available, easy to access and increasingly built into everyday business software. Employees can begin using them without specialist technical knowledge or support from an IT department.<\/p>\n\n\n\n Many employees use AI because they believe it will help them complete tasks more efficiently.<\/p>\n\n\n\n A marketing professional may use an AI assistant to prepare campaign ideas, while a software developer may use a coding assistant to troubleshoot an application.<\/p>\n\n\n\n When employees cannot access suitable approved tools, they may choose alternatives independently.<\/p>\n\n\n\n Traditional software approval processes may involve procurement, security reviews, legal assessments and budget approvals.<\/p>\n\n\n\n These processes remain important, but they can be difficult to apply to an AI market where new tools and models appear frequently.<\/p>\n\n\n\n If approval takes too long, employees may experiment outside official channels.<\/p>\n\n\n\n Shadow AI does not always involve employees deliberately installing new software.<\/p>\n\n\n\n An existing SaaS application may introduce AI features that change how information is processed or shared.<\/p>\n\n\n\n Organisations need to understand these changes because previously approved software can gain new AI capabilities that require further review.<\/p>\n\n\n\n Employees may use personal subscriptions or free AI services for work-related tasks.<\/p>\n\n\n\n These accounts often sit outside company identity systems, procurement records and monitoring tools.<\/p>\n\n\n\n As a result, organisations may not know which services are being used or what business information employees are sharing.<\/p>\n\n\n\n Quick link:<\/strong> AI Cost Governance for Healthcare: HIPAA-Aware AI Usage Tracking<\/a><\/em><\/p>\n\n\n\n The risks depend on the tool, its permissions, the information being processed and the business activity involved.<\/p>\n\n\n\n However, several concerns appear repeatedly across organisations.<\/p>\n\n\n\n Employees may submit confidential information to external AI services without understanding the provider’s data handling terms.<\/p>\n\n\n\n This could include customer records, financial reports, source code, internal strategy documents or employee information.<\/p>\n\n\n\n Once information leaves approved systems, the organisation may have limited ability to control its processing, retention or further use.<\/p>\n\n\n\n Organisations have responsibilities concerning personal data and regulated information.<\/p>\n\n\n\n For example, the UK GDPR<\/a> requires appropriate protection of personal data, while the EU AI Act<\/a> introduces specific obligations for certain AI systems and organisations.<\/p>\n\n\n\n Using an unapproved AI service does not automatically create a legal violation. However, it can make it harder to assess whether the relevant requirements are being met.<\/p>\n\n\n\n Shadow AI can also create financial problems.<\/p>\n\n\n\n Different departments may purchase overlapping subscriptions, developers may use expensive models without spending controls, and automated workflows may generate unexpected API costs.<\/p>\n\n\n\n Without central visibility, finance and engineering teams can struggle to understand total AI expenditure.<\/p>\n\n\n\n When an AI tool operates outside approved systems, it may be difficult to identify who owns it or who is responsible for reviewing its output.<\/p>\n\n\n\n This becomes particularly important when AI influences customer communications, business decisions or operational processes.<\/p>\n\n\n\n Some AI tools request access to company files, email accounts, cloud storage, code repositories or business applications.<\/p>\n\n\n\n If these permissions are not reviewed, an external service may receive more access than it needs.<\/p>\n\n\n\n AI agents create additional concerns because they may be able to perform actions rather than simply generate text.<\/p>\n\n\n\n Unapproved AI tools may produce incorrect, incomplete or misleading information.<\/p>\n\n\n\n If employees use those outputs without appropriate checks, mistakes can affect customers, operations and business decisions.<\/p>\n\n\n\n Organisations need clear rules for when human review is required, especially for important or sensitive workflows.<\/p>\n\n\n\n Detecting Shadow AI requires a combination of technical monitoring, employee engagement and business process reviews.<\/p>\n\n\n\n No single discovery method can identify every AI tool being used across an organisation.<\/p>\n\n\n\n The most effective approach combines several sources of evidence.<\/p>\n\n\n\n Start by documenting the AI tools and services already approved for business use.<\/p>\n\n\n\n The inventory should include standalone AI applications, embedded AI features, API integrations, internal models and autonomous agents.<\/p>\n\n\n\n For each system, record its business purpose, owner, provider, model or service, users, connected data sources, access permissions, spending and approval status.<\/p>\n\n\n\n This creates a reference point against which new or unexpected activity can be assessed.<\/p>\n\n\n\n Without an inventory, teams cannot reliably distinguish approved AI usage from activity that needs further investigation.<\/p>\n\n\n\n Network security tools can help organisations identify connections to known AI services.<\/p>\n\n\n\n Secure web gateways, DNS logs and other approved network monitoring systems may reveal access to AI websites or APIs.<\/p>\n\n\n\n For example, security teams may identify repeated connections to an AI provider that does not appear in the approved application inventory.<\/p>\n\n\n\n However, network activity alone does not prove that confidential information was shared or that an employee violated company policy.<\/p>\n\n\n\n Some traffic may come from approved software, background services or legitimate research.<\/p>\n\n\n\n The findings should therefore be reviewed in context.<\/p>\n\n\n\n Employees increasingly use AI features inside existing SaaS products.<\/p>\n\n\n\n Security and IT teams should review which applications are connected to company accounts and what permissions those applications have received.<\/p>\n\n\n\n OAuth application reviews, SaaS security tools and identity management systems can help identify third-party services accessing business resources.<\/p>\n\n\n\n This is especially important when AI applications request permission to read documents, access email or interact with cloud storage.<\/p>\n\n\n\n The goal is to identify tools that require review before they gain unnecessary access to sensitive systems.<\/p>\n\n\n\n Developers may connect directly to AI providers through API keys.<\/p>\n\n\n\n If these integrations are created without central approval, organisations may have limited visibility into their usage and spending.<\/p>\n\n\n\n Teams should review approved AI provider accounts, projects, API keys and application integrations.<\/p>\n\n\n\n Useful metrics include request volume, token consumption, model selection, spending, application ownership and unusual changes in activity.<\/p>\n\n\n\n Central monitoring can help identify unexpected workloads within connected AI environments.<\/p>\n\n\n\n However, an important limitation remains: monitoring approved API gateways or connected provider accounts will not automatically reveal AI usage through personal accounts, unmanaged networks or providers that are not connected to the monitoring system.<\/p>\n\n\n\n These gaps require additional discovery methods.<\/p>\n\n\n\n AI-powered browser extensions and desktop applications can introduce additional data access risks.<\/p>\n\n\n\n Some extensions may request permission to read webpage content, access browser activity or interact with business applications.<\/p>\n\n\n\n IT teams should review installed applications and extensions on managed devices, using appropriate endpoint management and security tools.<\/p>\n\n\n\n Policies should define which categories of extensions require approval and how employees can request access to useful new tools.<\/p>\n\n\n\n Not all Shadow AI activity appears in technical monitoring systems.<\/p>\n\n\n\n Some departments may purchase AI subscriptions using corporate cards, departmental budgets or expense claims.<\/p>\n\n\n\n Finance and procurement teams can help identify subscriptions that have not completed the organisation’s normal review process.<\/p>\n\n\n\n This also helps uncover duplicate spending across teams.<\/p>\n\n\n\n An organisation may discover that several departments are paying separately for similar AI capabilities that could be managed through an approved enterprise arrangement.<\/p>\n\n\n\n Employee surveys and interviews are often overlooked as discovery methods.<\/p>\n\n\n\n Employees may be more willing to share their AI usage when they understand that the purpose is to improve access to useful tools rather than automatically punish experimentation.<\/p>\n\n\n\n Ask teams which AI tools they use, what tasks they perform, what information they process and which approved tools they find inadequate.<\/p>\n\n\n\n These conversations can reveal important gaps in the organisation’s technology strategy.<\/p>\n\n\n\n Shadow AI often indicates unmet business needs, not simply poor employee behaviour.<\/p>\n\n\n\n Quick link:<\/strong> Top AI Observability Platforms of All Time<\/a><\/em><\/p>\n\n\n\n Once organisations understand where unapproved AI tools are being used, the next step is establishing practical controls.<\/p>\n\n\n\n A complete ban may appear simple, but it can encourage employees to hide useful experimentation rather than bring it into approved systems.<\/p>\n\n\n\n A more effective approach combines clear rules with access to suitable tools.<\/p>\n\n\n\n Every organisation should establish an AI acceptable use policy that employees can understand and follow.<\/p>\n\n\n\n The policy should explain which AI tools are approved, what information employees may share, which activities require additional review and who is responsible for AI-generated outputs.<\/p>\n\n\n\n Employees should understand whether they can use AI for writing, research, coding, customer support, document processing and other common activities.<\/p>\n\n\n\n The policy should also explain how employees can request new tools.<\/p>\n\n\n\n A policy that simply tells employees to use AI responsibly without defining acceptable behaviour provides little practical guidance.<\/p>\n\n\n\n Employees often turn to Shadow AI because existing company tools do not meet their needs.<\/p>\n\n\n\n If organisations want to reduce unapproved usage, they should provide secure and useful alternatives.<\/p>\n\n\n\n These may include enterprise AI assistants, approved coding tools, centrally managed model access or internal AI applications.<\/p>\n\n\n\n The organisation should also review employee feedback to understand where approved solutions need improvement.<\/p>\n\n\n\n The objective is to make the approved route easier and more useful than the unapproved alternative.<\/p>\n\n\n\n AI tools should receive only the access needed to perform their approved tasks.<\/p>\n\n\n\n Identity management, role-based permissions and application access reviews can help reduce unnecessary exposure.<\/p>\n\n\n\n For example, an AI assistant used to summarise public documents should not automatically receive permission to read confidential financial records.<\/p>\n\n\n\n For AI agents, organisations should establish clear limits on which tools they can call and which actions require human approval.<\/p>\n\n\n\n These controls should be reviewed whenever the application changes.<\/p>\n\n\n\n Once AI tools have been approved, organisations need ongoing visibility into their operation.<\/p>\n\n\n\n Monitoring should help teams understand which models are being used, which applications generate requests, how much activity occurs and whether unexpected usage patterns appear.<\/p>\n\n\n\n Cost monitoring is important, but it should be combined with operational information.<\/p>\n\n\n\n For example, an unexpected increase in token consumption may indicate legitimate growth, an application error or an agent repeatedly performing the same task.<\/p>\n\n\n\n Teams should also review model changes, unusual API key activity, workload ownership and routing behaviour.<\/p>\n\n\n\n Central monitoring helps organisations manage approved AI environments more consistently.<\/p>\n\n\n\n Financial controls can help organisations reduce unmanaged AI spending.<\/p>\n\n\n\n Teams should establish budgets for departments, applications and agents where appropriate.<\/p>\n\n\n\n Usage alerts can identify unexpected consumption before it creates significant costs.<\/p>\n\n\n\n Organisations should also review duplicate subscriptions and determine whether some workloads can be managed more efficiently through approved shared services.<\/p>\n\n\n\n However, cost limits should be designed around business requirements. Automatically disabling a critical production application when it reaches a spending threshold could create operational problems.<\/p>\n\n\n\n Employee education is essential because many Shadow AI risks begin with everyday decisions.<\/p>\n\n\n\n Training should explain how AI tools handle information, why confidential data requires protection and how to check AI-generated outputs.<\/p>\n\n\n\n Employees should also understand the risks of connecting AI applications to company accounts and granting broad permissions.<\/p>\n\n\n\n Training is more effective when it reflects tasks employees actually perform.<\/p>\n\n\n\n For example, explain the difference between using AI to improve a public marketing headline and uploading a confidential customer contract into an unapproved service.<\/p>\n\n\n\n The aim is to help employees make informed decisions rather than discourage them from using AI.<\/p>\n\n\n\n Organisations should make it straightforward for employees to request new AI tools.<\/p>\n\n\n\n A practical review should consider the business purpose, data involved, vendor security, privacy terms, access permissions, costs and expected benefits.<\/p>\n\n\n\n Low-risk tools may be suitable for a simpler approval process, while systems processing sensitive information or performing autonomous actions may need more detailed assessments.<\/p>\n\n\n\n A faster, risk-based approval process can reduce the incentive to adopt tools outside official channels.<\/p>\n\n\n\n Shadow AI discovery should not be treated as a one-time audit.<\/p>\n\n\n\n New tools appear regularly, existing software gains AI capabilities and employees find new ways to automate their work.<\/p>\n\n\n\n Organisations should therefore review AI inventories, vendor approvals, application permissions, usage reports and employee feedback on a regular basis.<\/p>\n\n\n\n They should also define how to respond when an unapproved tool is discovered.<\/p>\n\n\n\n Depending on the risk, the organisation may approve the tool, restrict its permissions, migrate users to an approved alternative or block further use.<\/p>\n\n\n\n The response should reflect the actual risk rather than assuming every unapproved application presents the same level of danger.<\/p>\n\n\n\n Shadow AI management should form part of a wider AI governance programme.<\/p>\n\n\n\n The NIST AI Risk Management Framework<\/a> provides a voluntary approach organised around four functions: Govern, Map, Measure and Manage.<\/p>\n\n\n\n These functions are relevant to Shadow AI because organisations need to establish responsibilities, identify AI systems and risks, assess their impact and take appropriate action. <\/p>\n\n\n\n ISO\/IEC 42001 also provides requirements for establishing and continually improving an AI management system.<\/p>\n\n\n\n For organisations processing personal data, privacy obligations remain important. The UK’s Information Commissioner’s Office provides guidance on applying data protection principles to AI development and deployment, including risk assessment and organisational accountability. <\/p>\n\n\n\n These frameworks and guidance resources can support a more structured approach, but adopting a framework does not automatically prove that every AI application is secure or compliant.<\/p>\n\n\n\n A strong Shadow AI programme can be organised around four stages.<\/p>\n\n\n\n Identify AI applications through inventories, network activity, SaaS reviews, endpoint management, procurement records and employee feedback.<\/p>\n\n\n\n Review each discovered tool according to its business purpose, data access, security, privacy, permissions and operational risk.<\/p>\n\n\n\n Approve suitable tools, restrict unnecessary permissions, provide safer alternatives and establish clear usage policies.<\/p>\n\n\n\n Track approved AI activity, review unexpected changes, maintain ownership and update controls as applications evolve.<\/p>\n\n\n\n This approach allows organisations to reduce risk while continuing to benefit from AI innovation.<\/p>\n\n\n\n WrangleAI<\/a> helps organisations gain greater visibility and control over their AI infrastructure.<\/p>\n\n\n\n As businesses adopt multiple models, providers and AI agents, understanding how those systems are being used becomes increasingly difficult.<\/p>\n\n\n\n WrangleAI brings together capabilities for AI usage monitoring<\/a>, cost visibility<\/a>, key management, budget controls and intelligent model routing.<\/p>\n\n\n\n These capabilities can help engineering and governance teams understand which workloads are consuming AI resources, identify unusual usage patterns and improve accountability across connected AI environments.<\/p>\n\n\n\n WrangleAI can also support more consistent operational controls by helping organisations manage AI access and spending through a central layer.<\/p>\n\n\n\n However, it is important to distinguish between monitoring connected AI infrastructure and discovering every instance of Shadow AI.<\/p>\n\n\n\n A platform that monitors approved providers, keys or gateways cannot automatically identify employees using personal AI accounts or unmanaged applications outside those systems.<\/p>\n\n\n\n Organisations should therefore combine WrangleAI’s operational visibility with wider security discovery, identity management, employee education and vendor approval processes.<\/p>\n\n\n\n Together, these measures can support a more complete approach to AI governance.<\/p>\n\n\n\n Shadow AI<\/strong> is becoming an important challenge for organisations as artificial intelligence spreads across everyday business operations.<\/p>\n\n\n\n Employees want to use AI to work faster, but unapproved tools can create gaps in security, privacy, spending and accountability.<\/p>\n\n\n\n The solution is not simply to block AI adoption. Organisations need to understand which tools employees are using, assess their risks, provide approved alternatives and maintain appropriate visibility over AI activity.<\/p>\n\n\n\n A strong Shadow AI strategy combines discovery, clear policies, access controls, employee education, financial governance and continuous monitoring.<\/p>\n\n\n\n WrangleAI helps strengthen the operational side of this strategy by giving organisations greater visibility into AI usage, costs, models, keys and agents across supported environments.<\/p>\n\n\n\n By combining central AI monitoring with wider governance practices, businesses can reduce unmanaged AI risks while giving employees the tools they need to work effectively.<\/p>\n\n\n\nWhat Is Shadow AI?<\/h2>\n\n\n\n
Why Shadow AI Is Becoming More Common<\/h2>\n\n\n\n
Employees Want to Work Faster<\/h3>\n\n\n\n
AI Adoption Is Moving Faster Than Internal Policies<\/h3>\n\n\n\n
AI Features Are Appearing Inside Existing Applications<\/h3>\n\n\n\n
Personal Accounts Make Visibility Difficult<\/h3>\n\n\n\n
What Are the Main Risks of Shadow AI?<\/h2>\n\n\n\n
1. Exposure of Sensitive Business Information<\/h3>\n\n\n\n
2. Privacy and Regulatory Compliance Risks<\/h3>\n\n\n\n
3. Uncontrolled AI Spending<\/h3>\n\n\n\n
4. Weak Accountability<\/h3>\n\n\n\n
5. Security and Access Risks<\/h3>\n\n\n\n
6. Poor Output Quality and Business Decisions<\/h3>\n\n\n\n
How to Detect Shadow AI Across Your Organisation<\/h2>\n\n\n\n
1. Build a Complete AI Tool Inventory<\/h2>\n\n\n\n
What Should You Record?<\/h3>\n\n\n\n
2. Review Network and Web Activity<\/h2>\n\n\n\n
3. Monitor SaaS Applications and Connected AI Tools<\/h2>\n\n\n\n
4. Analyse AI API Usage<\/h2>\n\n\n\n
Monitor Important Usage Metrics<\/h3>\n\n\n\n
5. Review Software and Browser Extensions<\/h2>\n\n\n\n
6. Examine Procurement and Expense Records<\/h2>\n\n\n\n
7. Ask Employees Which AI Tools They Use<\/h2>\n\n\n\n
How to Control Shadow AI Without Blocking Innovation<\/h2>\n\n\n\n
8. Create a Clear AI Acceptable Use Policy<\/h2>\n\n\n\n
Make the Policy Practical<\/h3>\n\n\n\n
9. Provide Approved Alternatives<\/h2>\n\n\n\n
10. Apply Access and Permission Controls<\/h2>\n\n\n\n
11. Centralise Approved AI Usage Monitoring<\/h2>\n\n\n\n
Monitor More Than Costs<\/h3>\n\n\n\n
12. Introduce AI Budget Controls<\/h2>\n\n\n\n
13. Train Employees on AI Risks<\/h2>\n\n\n\n
Use Real Business Examples<\/h3>\n\n\n\n
14. Establish a Fast AI Approval Process<\/h2>\n\n\n\n
15. Review Shadow AI Risks Continuously<\/h2>\n\n\n\n
Shadow AI and AI Governance Frameworks<\/h2>\n\n\n\n
A Practical Shadow AI Management Strategy<\/h2>\n\n\n\n
Discover<\/h3>\n\n\n\n
Assess<\/h3>\n\n\n\n
Control<\/h3>\n\n\n\n
Monitor<\/h3>\n\n\n\n
How WrangleAI Helps Organisations Manage AI Usage and Governance<\/h2>\n\n\n\n
Final Thoughts<\/h2>\n\n\n\n
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