By Kyle Hill, CTO at digital transformation company, ANS
Artificial intelligence (AI) is shifting from a promising tool to a central pillar of modern healthcare. With the NHS now trialling technologies like Microsoft Copilot and accelerating digital transformation, AI is set to reshape how clinicians manage information, support diagnosis and streamline patient pathways.
But as AI moves deeper into clinical and operational workflows, security concerns risk being overshadowed by the drive for rapid adoption.
Our latest research shows that only 35% of healthcare IT leaders currently prioritise security when implementing AI. For a sector that’s already an attractive target for cyber attacks, that gap is a serious threat to resilience and patient safety.
To ensure the sector can scale AI responsibly, it must treat security as a foundational requirement, not an afterthought once innovation has already taken place.
AI adoption in healthcare is outpacing AI cyber defences
The recently launched NHS Copilot trial shows just how impactful AI could be across frontline and administrative functions. With document summarisation and faster data processing, these AI tools can free up valuable clinician time and help alleviate mounting operational pressures.
However, rapid adoption without aligned security increases risk significantly. Each new AI-enabled workflow creates additional integration points and data flows, expanding the threat surface across already complex NHS systems. The healthcare sector has been the victim of disruptions, whether from system outages or cyber attacks. These can spread quickly and affect essential services.
That is why resilience is critical. A compromised model or AI-driven system failure would not only impact operations but could undermine trust in tools clinicians depend on.
New AI capabilities bring new risks
AI introduces a distinct class of vulnerabilities that traditional healthcare cybersecurity models were never designed around. Because AI systems continuously learn and adapt, they also create evolving risk profiles. Without strong oversight, AI tools can inadvertently expose sensitive information, generate inaccurate outputs, or be manipulated through data poisoning or model interference.
The complexity of digital health supply chains makes this even more challenging. AI systems often rely on third-party cloud services, external datasets, and open-source components. Any weakness in any part of that chain can compromise the wider system, even if the healthcare organisations maintain strong internal controls.
But it’s the very people who use the tech that are often the greatest risk. Clinicians and administrative staff are under pressure and may use AI tools without sufficient guidance on safe data handling. Without training, they may inadvertently share sensitive patient details with external AI systems or misinterpret AI outputs, which could lead to disastrous consequences.
For AI to be secure, the NHS needs visibility into how data moves, how AI systems make decisions and who is accountable for their protection.
Laying the security groundwork for safe AI adoption
To scale AI responsibly, healthcare organisations must approach security as an enabler of innovation rather than a constraint. Strong foundations mean the healthcare sector can deploy AI at speed without compromising safety or operational continuity.
Understand your security posture for AI
Security for AI begins with a full view of the system. That means assessing not only infrastructure but also training data, model behaviour, API & MCP tool connections, and integration points across clinical systems. By identifying where vulnerabilities could emerge, healthcare leaders can address small issues before they harm clinical services or patient privacy.
Embed security into AI strategy
With recent advancements and government investment in bolstering AI within the sector, adoption seems to be well underway. But its security must be treated with the same importance. By embedding cybersecurity into AI from the outset, those leading the AI implementation can ensure that risk management and compliance do not become barriers later in implementation, especially across safety-critical processes.
Build a culture of responsible AI use
Technology alone cannot secure AI. A shift in organisational culture is essential to help clinicians and staff use AI tools safely. When teams understand how AI systems work and what good risk management looks like, they can use the tools confidently without compromising patient safety.
Training is vital. Staff must know how to handle sensitive data and recognise when AI is being misused or manipulated. With the right support, employees become active defenders rather than unintentional points of vulnerability for AI use.
Treat security as a continuous requirement
Security doesn’t stop once AI is deployed. NHS systems operate in dynamic environments, and AI models change as they learn. Continuous monitoring helps ensure systems remain compliant and resilient as adoption grows.
By integrating security throughout the entire AI lifecycle, from concept and design to deployment, healthcare organisations build long-term confidence in the technology.
A secure future for AI in healthcare
AI’s potential to support clinicians and improve patient outcomes can change the game for the sector. But the benefits will only be realised if the technology is built on secure, trustworthy foundations. The healthcare sector can’t afford to scale AI on fragile or incomplete security frameworks.
When viewed as a strategic pillar of AI adoption, security becomes an essential platform that allows AI to reach its full potential, supporting clinicians and staff, and ultimately improving care for all patients.
