Understanding why staff use unapproved AI tools is key to managing the security challenges they can create.
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Over the past few years, the use of artificial intelligence (AI) has grown rapidly in many workplaces with employees increasingly exploring how such tools can be incorporated into their jobs.
AI can help people complete tasks more quickly, improve decision-making, save costs and increase productivity.
However, organisations’ policies and guidance, which should reflect and manage the risks associated with using these new technologies, have not always developed at the same pace.
Rather than preventing them from using AI, this can mean employees turn to using AI tools that have not been approved by their organisation, introducing new cyber security risks that can be hard to identify.
What is shadow AI?
Shadow AI describes the use of AI technology which isn’t captured in an organisation’s approved systems and processes. It is a form of shadow IT (or ‘grey IT’).
Recent research suggests that using shadow AI is widespread, with one study finding that nearly three-quarters of employees (71%) reported using AI tools that have not been approved by their employer.
Where cyber security policies cannot meet business needs, organisations are likely to continue seeing their employees adopt new AI services before they have had time to assess them and provide approved alternatives. This trend is likely to be reinforced as AI capabilities become increasingly affordable and readily available.
What are the cyber security risks of shadow AI?
The use of shadow AI can create risks that organisations may struggle to identify and in turn manage, potentially resulting in breaches and security incidents. For example:
- Sensitive information may be exposed
Providing shadow AI access to company or customer data likely increases the risk of data breaches, intellectual property loss and failure to meet regulatory requirements.
- Organisations can lose visibility and control of data
Employees who transfer sensitive or proprietary information to consumer AI services will likely reduce the organisation's visibility and control over that information. This is because that information may be stored, retained or used to improve the service – outside established security and governance arrangements – unless specific privacy controls are in place.
- New opportunities for attackers
AI agents are complex pieces of software that can have critical security vulnerabilities. If an attacker successfully exploits a vulnerability, they can gain access to the same data, services, and privileges that the agent has legitimate access to.
Attackers are highly likely to use agents with looser guardrails to exploit any vulnerabilities or misconfigurations in the wider corporate IT system.
Encourage staff to choose wisely
The NCSC is not recommending that individuals stop using AI – but when turning to these tools for assistance with a work task, think carefully about which apps and services you are using before you share data.
It may feel natural to stick with using the same AI service that you are familiar with from your personal life – but using systems that are not corporately approved can present real problems for your employer.
Focus on reducing the risk
For organisations, the challenge is ensuring that employees have access to AI tools that meet their needs while managing cyber risk appropriately.
The use of shadow AI is unlikely to disappear completely. As with shadow IT more broadly, the goal should be to reduce risk rather than assume it can be eliminated. To do this, organisations should:
- adopt a positive cyber security culture. Encouraging open communication about cyber security issues means employees are much less likely to turn to shadow IT services, including shadow AI. Organisations that understand why people are using shadow AI are better placed to identify risks, provide secure alternatives and support innovation safely
- securely integrate AI systems into the workplace. Refer to the NCSC and international partners' guidance on careful adoption of agentic AI services
You cannot manage what you do not know. By raising awareness of the risks of shadow AI use within your organisation and understanding the needs of employees, you can help them get the benefits of new technologies while using them securely.
Further reading
The NCSC’s cyber security culture principles.
An NCSC blog on thinking carefully before adopting agentic AI.
Simon B
Senior Cloud Researcher
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Senior Cloud Researcher
Facts Only
* Employees use AI tools that are not approved by their organization.
* One study found 71% of employees reported using unapproved AI tools.
* Shadow AI is defined as AI technology not captured in organizational systems and processes, categorized as shadow IT or 'grey IT'.
* Using shadow AI increases the risk of data breaches, intellectual property loss, and failure to meet regulatory requirements when sensitive information is provided to consumer services.
* Employees transferring proprietary information to consumer AI services reduce organizational visibility and control over that information if privacy controls are absent.
* AI agents can possess critical security vulnerabilities that attackers can exploit to gain access to data and privileges.
* The National Cyber Security Centre (NCSC) advises caution when using unapproved tools, focusing on careful assessment before sharing data.
* Organizations should adopt a positive cybersecurity culture and securely integrate AI systems into the workplace.
Executive Summary
The widespread adoption of unapproved Artificial Intelligence (AI) tools in workplaces, termed shadow AI, presents significant cybersecurity challenges because organizational policies often lag behind technological development. Employees utilize these unapproved tools to enhance productivity and decision-making, which bypasses established security protocols. This trend is fueled by the affordability and accessibility of AI capabilities, leading staff to adopt services before formal assessments and approved alternatives are in place.
The use of shadow AI introduces specific risks: exposure of sensitive information through data transfer to consumer AI services, loss of organizational visibility and control over proprietary data stored or used by external services outside governance arrangements, and the introduction of new attack vectors via potentially vulnerable AI agents exploited by malicious actors. Organizations must address this by fostering a positive security culture and securely integrating AI systems, rather than solely focusing on prevention.
Full Take
The narrative highlights a systemic tension between rapid technological adoption and slow institutional governance. The phenomenon of shadow AI illustrates a failure in risk management where employee necessity drives action outside established security frameworks, creating an information asymmetry that inherently favors potential exploitation. The core pattern observed is the lag between capability development and policy enactment, which permits risks to materialize in the interim. This structure suggests that control over innovation is being ceded to individual actors who operate outside centralized oversight.
The implication for human agency lies in the dilemma faced by employees who seek efficiency versus their institutional responsibility; they are placed in a position where necessary workarounds become potential liabilities managed implicitly rather than explicitly. The call for a positive security culture shifts the locus of control from purely punitive enforcement to shared responsibility, suggesting that resilience is built through understanding and communication rather than mere compliance. Further analysis should focus on whether the perceived immediate benefits of shadow AI effectively outweigh the abstract, long-term security exposure, and what structural changes are required to make secure adoption the path of least resistance for employees.
Bridge Questions: What mechanisms can organizations implement to accelerate policy development in response to emergent technologies? How can incentive structures be redesigned to reward proactive risk identification by employees rather than penalizing necessary workarounds? If shadow AI is accepted as an inevitable reality, what new governance paradigms must replace traditional perimeter-based security models?