Generative AI has quickly become part of everyday work. Marketing teams draft campaigns, developers troubleshoot code, HR teams summarise CVs, and finance teams analyse spreadsheets in minutes. Much of this happens outside approved enterprise systems. This growing use of shadow AI is changing how organisations think about security, governance, and data protection. The challenge is no longer whether employees use AI, but whether organisations can govern its use without slowing innovation.
What is shadow AI and why is it a visibility problem?
Shadow AI refers to employees using AI applications without the knowledge or approval of IT or security teams. Employees rarely adopt these unsanctioned AI tools with malicious intent. Most simply want to complete routine tasks faster. But once confidential information leaves approved business applications and enters public AI services, organisations lose visibility into how those services process, retain, or share the data.
The pace of adoption explains why governance has become difficult. A PagerDuty survey reports that 66% of employees who have used AI for work have used unsanctioned AI tools or services despite believing their company’s policies did not allow such use. That leaves organisations with limited oversight of how employees use AI at work.
Why is employee data sharing a key shadow AI risk?
Most security teams already monitor email, file transfers, and cloud applications. AI interactions create a different challenge because employees often include sensitive information in prompts rather than documents. An engineer asking an AI assistant to review source code or an HR manager uploading employee records for summarisation may not realise they are exposing confidential information outside approved controls.
The Shadow AI Surge in Enterprises: Insights from the U.S. and Canadian Workplace notes that 93% of employees admitted to inputting information into AI tools without approval. The information at risk extends well beyond customer data. Organisations regularly handle:
- Intellectual property
- Financial forecasts
- Contracts
- Pricing models
- Internal strategy documents
- Credentials
- Proprietary source code
Any of these can become part of an AI conversation if employees rely on unsanctioned AI tools without understanding their organisation’s policies or the AI provider’s data handling practices.
Why does an AI governance policy matter?
Many organisations initially responded by blocking access to public AI services. That approach rarely works for long. Employees often switch to personal accounts, browser extensions, or alternative AI applications. The result is even less visibility.
A stronger response is a well-defined AI governance policy that balances innovation with accountability. Instead of asking employees not to use AI, organisations should define which tools employees can use, what information employees can share, who is responsible for oversight, and how the organisation will monitor AI activity. The objective is responsible adoption rather than blanket restriction.
This balance is becoming increasingly important. According to Cyberhaven’s 2026 AI Adoption & Risk Report, 39.7% of information pasted into public AI tools contains sensitive or confidential data. Even a single prompt containing source code, customer information, or pricing data can create unnecessary business risk.
How should enterprises manage shadow AI?
Managing shadow AI is not a one-time technology project. AI applications appear, evolve, and disappear far more quickly than traditional enterprise software. Governance, therefore, needs ongoing oversight of AI usage across users, departments, and business functions.
A practical approach begins with discovering which AI applications employees are already using. Organisations can then classify those applications based on business value and risk, enforce policies for sensitive data, monitor AI interactions continuously, and educate employees about acceptable use. An effective AI governance policy combines technical controls with clear guidance, helping security teams protect sensitive data without becoming a barrier to productivity.
There is still debate about how restrictive organisations should be. Some security leaders advocate blocking all public AI platforms, while others favour secure enterprise AI environments with embedded monitoring and data loss prevention controls. The right approach depends on an organisation’s regulatory obligations, risk appetite, and the types of information it handles.
What matters is having enough visibility to make informed decisions. Organisations cannot govern AI they cannot detect.
How can Infosys BPM’s generative AI services support your business operations?
Infosys BPM helps organisations adopt generative AI responsibly by combining AI-powered process transformation with robust governance, intelligent automation, and data-driven decision-making. Through enterprise AI enablement, responsible AI frameworks, workflow optimisation, and scalable AI integration, Infosys BPM enables businesses to improve productivity, strengthen operational resilience, protect sensitive information, and accelerate AI adoption while maintaining governance and compliance across business operations.
Explore how Infosys BPM’s generative AI services can help your organisation integrate AI into business processes, establish responsible AI governance, and unlock greater business value through secure and scalable AI adoption.
Frequently asked questions
Shadow AI is the use of AI applications by employees without the knowledge or approval of IT or security teams. This use is rarely malicious. Most people are trying to work faster with a tool that their organisation has not yet sanctioned or approved.
Traditional monitoring watches email, file transfers, and cloud applications. AI exposure happens inside the prompt, where employees paste contract text, pricing, forecasts, credentials, or source code. Nothing is downloaded or attached, so conventional controls see a normal web session.
Rarely, and not for long. Employees move to personal accounts, browser extensions, or alternative applications, which removes the activity from view entirely. Blocking reduces visibility more reliably than it reduces usage.
An AI governance policy should define which tools are approved and for what purposes, what data may and may not be entered, when human review is required, how new tools are assessed, and who owns each decision. The policy should be paired with discovery so the organisation knows what is actually in use before it writes rules about it.
