Shadow AI happens when employees use AI tools for work without a clear company policy, approved tooling, data rules or operational oversight. It is the AI version of shadow IT: useful behaviour that grows outside the systems the company can see and govern.
The risk is not that employees are careless. Often the opposite is true. They are trying to work faster, summarise documents, draft messages, classify information or solve recurring problems. If the company does not provide a safe way to do that, people will find their own tools.
Why Shadow AI spreads quickly
AI tools are easy to access and immediately useful. A person can paste a contract excerpt into a chatbot, upload a spreadsheet for analysis, summarise a customer email thread or ask for a technical explanation in seconds. The value is obvious before the governance exists.
CPHD Nordic’s Shadow AI framing is useful because it focuses on loss of control. When employees use public AI tools with business information, the company may not know what data was entered, which provider processed it, whether prompts are logged, whether outputs were reused, or whether sensitive information crossed a boundary it should not have crossed.
NNIT makes a related point in its responsible AI adoption material: if organisations do not make approved, secure AI solutions easy to use, employees may choose unofficial alternatives. This makes governance a usability problem as much as a policy problem.
The real risks
Shadow AI can create several types of risk:
- confidential information entered into uncontrolled tools;
- customer or employee data processed without clear approval;
- business decisions influenced by unverified output;
- internal knowledge copied into tools without retention or access rules;
- inconsistent answers generated by different teams;
- no logging, audit trail or review process;
- no way to improve or monitor recurring AI use cases.
The most dangerous pattern is not experimentation. It is invisible operational dependency. If people begin to rely on unofficial AI workflows for recurring business tasks, the company has a hidden system with no owner.
Governance should not start with prohibition
A ban rarely solves the problem. It may simply push AI use further underground. A practical response should identify the tasks employees are already trying to improve.
Start by asking:
- Which AI tools are people using?
- What tasks are they using them for?
- What data are they entering?
- Which use cases are low risk and useful?
- Which use cases involve sensitive data?
- Which workflows should become approved internal tools?
- Which behaviours should be restricted or redesigned?
This creates a path from uncontrolled usage to approved capability.
Provide safe alternatives
A company can reduce Shadow AI by making safe AI workflows easier than unsafe ones. That may mean approved tools, private model access, role-based permissions, retrieval over approved document stores, logging, human review, prompt templates or system-connected workflow automations.
For example, instead of telling employees not to paste internal documents into a public tool, the company can provide an internal knowledge assistant that retrieves only approved documents and respects user access rights. Instead of letting each team classify incoming emails differently, it can build a shared workflow where AI suggests categories and humans approve exceptions.
Memory(One) perspective
Shadow AI is not only an AI governance topic. It is a software and workflow topic. Companies need approved systems that make useful AI adoption practical: connected to the right data, constrained by access control, visible to the organisation and designed with human review where judgement matters.
The right first step is usually not a large AI programme. It is an AI readiness review: where is AI already being used, what workflows could benefit, what data boundaries matter, and which use cases should become safe, system-connected tools?
Sources and inspiration
- CPHD Nordic — Shadow AI is your biggest risk: https://www.cphdnordic.com/indsigter/skygge-ai-er-jeres-stoerste-risiko-saadan-faar-i-kontrol
- NNIT — AI in the public sector: from pilots to responsible operations: https://www.nnit.com/insights/articles/ai-in-the-public-sector-from-pilots-to-responsible-operations
- OWASP — Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/
- NIST — AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework