AI adoption rarely begins with a formal project plan. It starts when someone is trying to get through a busy afternoon.
An employee asks an AI tool to tidy up a customer email. A manager uses it to summarise meeting notes. A developer tests an AI coding assistant. If the tool has not been approved, assessed or included in the organisation’s processes, AI has entered the business through the side door.
That is Shadow AI.
Shadow AI is the use of artificial intelligence tools outside an organisation’s approved systems, policies and oversight.
It is not usually malicious. Most people are trying to solve a problem with the tools available to them. They may not know which AI services are approved, or they may feel that the official process is too slow for an urgent need.
Shadow AI should not be treated only as a compliance failure. It is also a signal. It shows where employees see opportunities to work more effectively, and where current AI guidance may not be keeping pace with everyday work.
An unapproved tool can create questions the business cannot easily answer:
If nobody owns those questions, the risk is already being managed by assumption.
An employee might paste a customer list, pricing model, contract or source code into a free AI tool to summarise, rewrite or analyse it. The intention may be harmless, but the data has now left the organisation’s controlled environment.
Depending on the tool, information may be retained, used to improve the service, accessed by people outside the organisation or exposed through a compromised account, insecure integration or data breach.
The organisation may no longer know who can access the information, where copies exist or whether they can be deleted. In the wrong conditions, sensitive business data can create security, privacy, contractual or reputational risk.
AI services may process information in different regions, pass it to other providers or handle inputs according to settings and terms that employees do not understand.
Depending on the service, prompts and uploaded files may also be retained or used to improve the system. The organisation may not know how long the information is kept, who can access it, whether copies exist or how it can be deleted.
AI can produce an answer that sounds convincing but contains a made-up fact, incorrect calculation or missing context.
If that output is copied into a customer response, proposal, legal document, financial decision or employee record, the business remains accountable for the result.
Human review should be more than a quick glance. The reviewer needs enough context to check the source information, question the result and correct it before it affects someone else.
The risk increases when AI can do more than generate text. An AI agent may retrieve files, update records, send messages or trigger workflows.
If it has more access than it needs, a mistake, poor configuration or hidden instruction in a document or email could lead to an unintended action.
Treat agents like applications that can access data and take action. Give them only the permissions they need, require approval for high-impact actions and keep a record of what they do.
When an employee uses a personal account, browser extension or unapproved integration, the organisation may not have the access controls, logs or vendor agreement needed to investigate a problem.
It may also be unclear who approved the use case, who checked the output or who must respond if sensitive information has been exposed.
This is why Shadow AI is a business issue, not only an IT issue. It can affect privacy, confidentiality, intellectual property, customer trust, contractual obligations and the quality of business decisions.
The question for leaders is not only:
The question for leaders is not only:
“Did someone use AI?”
It is:
“Where is AI receiving business information, making recommendations or taking action, and what controls are in place?”
The instinctive response to Shadow AI is often to ban it. That may feel reassuring, but it does not necessarily make the organisation safer.
People still have deadlines and want to reduce repetitive work. If approved options are unavailable or difficult to use, experimentation tends to continue quietly through personal accounts, browser extensions and unassessed integrations.
The result is less visibility, not less AI.
A more useful approach is to make the safe path easy to follow. That means understanding what people are trying to do, providing approved tools that support legitimate work and setting clear boundaries around data, access and accountability.
Before writing a policy, talk to the people already using AI.
Ask which tools they use, which tasks they are supporting and what help they need. Keep the conversation constructive. If people expect blame, they are less likely to share what is happening.
The goal is visibility. You cannot manage a use case you do not know exists.
Most employees do not need a long AI policy. They need to know what information is safe to enter into an AI tool.
Public information is usually fine in approved tools. Private or sensitive information should only be used with permission. Passwords and other secrets should never be entered into an AI service.
Remember that AI-created summaries and recommendations may also contain sensitive information.
An AI tool is not automatically safe or unsafe for every task.
A service that is suitable for brainstorming public marketing ideas may not be appropriate for processing customer records.
An approved AI catalogue should explain what information each tool can handle, who owns it, how to request access and where to go with questions. Approval should match the level of risk.
AI agents will increasingly retrieve information, call systems and take actions on behalf of users.
An agent should have only the permissions it needs, and only for as long as it needs them.
High-impact actions, such as sending an external message, changing a record or deleting data, should require human confirmation. Use test data wherever possible during experimentation.
AI can draft, compare, summarise and suggest. It should not quietly become the decision-maker by default.
Define where human review is required, particularly for decisions involving employment, finance, legal matters, health, safety, security or customer outcomes.
Human review must be meaningful. The reviewer needs enough context to understand what the AI has done and enough authority to challenge it.
An AI policy is not finished when it is published. Monitor approved usage in proportion to the risk and revisit the programme regularly. Look for new tools, unusual access, repeated errors, near misses and areas where controls are making legitimate work difficult.
The useful question is not only, “Did someone break the policy?” It is also, “What made the safer option difficult to follow?” That question helps the organisation improve its guidance as people find new ways to use AI.
Shadow AI is not going to disappear completely. Nor should curiosity and experimentation.
The aim is to bring useful AI use into the light, where it can be supported, secured and improved.
CORE brings together practical guidance, clear guardrails and better ways of working, so your people can use AI safely and effectively.
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