Shadow AI: Understanding the Risks and Implications for the NHS

Organisations across many sectors are reporting a similar trend. Despite significant investment in official AI platforms, staff continue to use personal or consumer AI tools to support their day to day work. This informal pattern, often described as Shadow AI, refers to the use of unapproved AI applications outside an organisation’s governance and security framework. The trend is now visible within health and care settings, where pressures on time and administrative workload create strong incentives to seek faster, more convenient digital support.

Shadow AI use does not generally stem from deliberate rule breaking. Staff often turn to these tools because they offer ease of use, immediate results and minimal barriers to entry. By contrast, corporate systems can sometimes feel slower or less intuitive. For NHS staff managing complex clinical notes, correspondence, or administrative tasks, consumer AI tools can appear to offer quick solutions.

Risks Associated With Shadow AI

While this behaviour may appear practical, it presents several risks. The most significant is data exposure. When staff enter information into a public AI model, that data may be processed or stored outside secure environments. In the NHS, this could involve patient identifiable data, operational information, or commercially sensitive content. Any disclosure of such information, even inadvertently, could constitute a breach of GDPR, the Data Protection Act, or NHS information governance standards.

There are also regulatory considerations. Many public AI tools do not meet the security, audit or data residency requirements expected of NHS systems. This creates challenges for accountability and compliance. If sensitive data is entered into an unapproved system, the organisation remains responsible for any resulting breaches or regulatory actions.

Finally, Shadow AI reduces visibility. When staff use tools outside approved channels, NHS organisations cannot monitor how data is used, whether outputs are reliable, or whether algorithms introduce bias into clinical or operational workflows.

Implications for the NHS

For the NHS, the growth of Shadow AI has several implications.

First, there is a clear need for updated digital governance. Existing policies may not fully account for the capabilities or widespread availability of modern AI tools. NHS organisations may need to develop more detailed guidance on appropriate use, restrictions, and expectations for staff. Second, the trend highlights a gap between workforce needs and available tools. If staff rely on unapproved AI because official systems are not meeting operational demands, organisations may need to consider investing in secure, NHS compliant AI solutions that support administrative efficiency and clinical workflows. Third, training and awareness will become increasingly important. Staff should understand both the potential value of AI and the limits imposed by information governance. Effective training can reduce the risk of accidental breaches and improve confidence in using approved systems.

Fourth, the issue creates a financial and operational risk. Data breaches in healthcare are costly and can lead to reputational and regulatory consequences. Reducing Shadow AI use is therefore not only a governance concern but a cost avoidance measure.

A System-Level Consideration

At a wider level, the NHS will need to consider how AI governance is coordinated across trusts and integrated care systems. Variation in local policy could create inconsistencies in risk management. National guidance may be required to support alignment, especially as AI becomes more embedded in clinical and administrative pathways.

Shadow AI is likely to remain a feature of modern working environments. The challenge for the NHS will be to ensure that innovation continues in a managed and secure way, with appropriate controls and accessible tools that meet the needs of staff and protect patient data.