By Kyle Hill, CTO of a digital transformation company, YEAR
Artificial intelligence (AI), from a promising tool, is becoming a central pillar of modern healthcare. With the NHS now testing technologies such as Microsoft Copilot and accelerating digital transformationAI is poised to reshape the way clinicians manage information, support diagnosis, and streamline the patient journey.
But as AI becomes more established in clinical and operational workflows, security concerns risk being overshadowed by the drive for rapid adoption.
Our last research shows that only 35% of healthcare IT leaders are currently prioritizing security when implementing AI. For a sector that is already an attractive target for cyberattacks, this gap poses a serious threat to resilience and patient safety.
To ensure the industry can scale AI responsibly, it must view security as a fundamental requirement, not an afterthought after innovation has already taken place.
AI adoption in healthcare outpaces AI-related cyber defenses
The recently launched NHS Copilot trial shows how AI could impact frontline and administrative functions. Through document summarization and faster data processing, these AI tools can free up valuable clinician time and help alleviate increasing operational pressure.
However, rapid adoption without aligned security significantly increases risk. Each new AI-enabled workflow creates additional integration points and data feeds, widening the threat surface on already complex NHS systems. The healthcare sector has been subject to disruptions, whether due to system failures Or cyberattacks. These can spread quickly and affect essential services.
This is why resilience is essential. A compromised model or failure of an AI-driven system would not only impact operations, but could also undermine trust in the tools clinicians depend on.
New AI capabilities bring new risks
AI introduces a distinct class of vulnerabilities than traditional systems healthcare cybersecurity the models were never designed around it. Because AI systems are constantly learning and adapting, they also create evolving risk profiles. Without rigorous oversight, AI tools can inadvertently expose sensitive information, generate inaccurate results, or be manipulated through data poisoning or model interference.
The complexity of digital healthcare supply chains makes this task even more difficult. AI systems often rely on third-party cloud services, external datasets and open source components. Any weakness in any part of this chain can compromise the system as a whole, even if healthcare organizations maintain strict internal controls.
But it’s often the people who use technology who are at 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 information with external AI systems or misinterpret AI results, which could lead to disastrous consequences.
For AI to be secure, the NHS needs visibility into how data flows, how AI systems make decisions and who is responsible for protecting it.
Laying the Security Foundation for Safe AI Adoption
To scale AI responsibly, healthcare organizations must view security as an enabler of innovation rather than a constraint. Strong foundations enable the healthcare industry to deploy AI quickly without compromising security or operational continuity.
Understanding your AI security posture
AI security starts with a comprehensive view of the system. This means evaluating not only infrastructure, but also training data, model behavior, API and MCP tool connections, and integration points into clinical systems. By identifying where vulnerabilities might appear, healthcare leaders can address small issues before they harm clinical services or patient privacy.
Integrate security into AI strategy
With recent advancements and government investments to strengthen AI in the sector, its adoption appears well underway. But its safety must be treated with equal importance. By integrating cybersecurity into AI from the start, those leading AI implementation can ensure that risk management and compliance do not become obstacles later in the implementation, particularly in safety-critical processes.
Building a culture of responsible use of AI
Technology alone cannot secure AI. A change in organizational 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 with confidence without compromising patient safety.
Training is vital. Staff must know how to handle sensitive data and recognize when AI is being misused or manipulated. With the right support, employees become active advocates rather than unintended points of vulnerability for AI use.
View security as an ongoing 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 AI lifecycle, from concept and design to deployment, healthcare organizations build long-term confidence in the technology.
A secure future for AI in healthcare
The potential for AI to support clinicians and improve patient outcomes can be a game-changer for the industry. But the benefits will only be realized if the technology is built on a secure and reliable foundation. The healthcare industry cannot afford to deploy AI on weak or incomplete security frameworks.
Seen as a strategic pillar of AI adoption, security becomes a critical platform that enables AI to reach its full potential, supporting clinicians and staff and ultimately improving care for all patients.
