Across almost every commercial sector, we’re already reaping the benefits of AI. However, certain high-risk fields require more comprehensive security measures. In MedTech and Healthcare tech, AI is already reshaping patient care – from faster diagnosis and risk scoring to robotic-assisted surgery and continuous monitoring. But, while AI software development is moving at startup speed, security and safety practices are moving at regulatory speed.
This gap between innovation and safety isn’t theoretical. It’s where patients get hurt and regulatory submissions get delayed. In the current landscape, medical device manufacturers cannot afford to treat cybersecurity as a secondary IT concern. The leaders of tomorrow will be those who treat AI safety as a core clinical requirement, just as vital as hardware reliability.
Understanding the risk: assistive vs autonomous AI
One of the most common mistakes is lumping all “AI” into a single risk category. In reality, the risk profile is dependent on the level of autonomy granted to the system and how it is used. We must distinguish between assistive AI and autonomous AI.
Devices with assistive AI act like a second set of eyes. These could be applications that enhance low-resolution images for radiologists’ workflow triage and administrative scheduling. In these scenarios, the risk is lower because a human expert remains the final decision maker. This type of AI can often be deployed safely with the right controls and human oversight.
High-risk applications are different. The stakes change significantly with autonomous AI, where the system drives therapy decisions, guides life-sustaining devices, or informs surgical actions in near real time.
Consider an AI-driven triage system that automatically prioritizes urgent cases in an emergency department. If this model is compromised, the security failure is no longer just a technical “bug.” It turns into misdiagnosis, inappropriate treatment and delayed care. To secure these high-stakes environments, manufacturers must apply a risk-weighted lens to their entire development lifecycle.
The transparency problem
A significant challenge in AI-enabled MedTech is the “Black Box” problem. In traditional software, we can audit the code to find exactly where a logic error occurred. With deep learning models, the “why” behind a decision is often buried under millions of mathematical parameters.
This lack of transparency itself is a security risk. If a device malfunctions or gives a wrong diagnosis, we must be able to establish whether it was a hardware glitch, a software error, or a cyberattack. If we expect doctors and nurses to rely on AI-enabled devices, we owe them transparency on how those systems were trained, monitored, and secured.
Defending against model poisoning, algorithmic bias & diversion attacks
The threat landscape for medical AI has moved far beyond traditional malware. One of the biggest emerging risks is model and data poisoning. If an attacker or even a flawed process introduces bad data into a model’s training or update pipeline, the model can quietly shift its behavior.
A medical imaging model might start missing subtle tumors, or a risk-scoring system might under-prioritize certain patients. There’s no red flashing light when this happens. Performance just quietly degrades in real-world clinical settings, which may lead to very real consequences for patients and providers.
Right next to model and data poisoning is algorithmic bias. We’ve seen the headlines about biased hiring and credit models, but the same mechanics apply to MedTech. A model trained mostly on one demographic will underperform on other populations. That’s not just a fairness issue – it’s a safety issue. Manufacturers need to monitor for “performance drift” to ensure their models remain accurate for all patients over time.
We must also defend against inversion attacks. These target data privacy, not just model logic. An attacker “interrogates” the AI with specific queries and then analyse the responses to reverse-engineer sensitive training data. In a medical context, this could allow a malicious actor to reconstruct proprietary clinical markers or even sensitive patient records, making the model a portal for data leaks.
Engineering security from the start
As an industry, as AI integration accelerates, it is imperative that we move towards a security-by-design mindset, transitioning from a ‘nice-to-have’ language in slide decks to a non-negotiable expectation. For years, cybersecurity has been treated as a “bolt-on,” added late in development. This approach is already responsible for costly regulatory submission delays and last-minute redesigns. With AI, the cost and complexity of that mindset are no longer sustainable.
Security-by-design for AI means bringing threat modeling and risk assessment into the requirements phase, not bolting it on after the architecture is locked. It means treating training data, model pipelines, update mechanisms, and hospital integration points as part of the attack surface. This includes adopting adversarial robustness testing, where manufacturers intentionally try to “trick” or hack their own models during R&D. True safety requires continuous monitoring of the device throughout its entire lifecycle, not just pre-market testing.
AI as a defensive asset
However, the rise of AI also offers promising new ways to protect patient safety. AI is increasingly used to defend other AI systems in MedTech. Static controls and annual penetration tests are not enough when models and data flows are constantly changing.
We’re witnessing the beginning of a broader adoption of AI-driven monitoring systems that learn what “normal” looks like for a device. These tools monitor traffic patterns, usage, and outputs to flag anomalies that may indicate poisoning, tampering, or misuse. These systems won’t replace human judgment, but they are giving security and clinical teams early warning before subtle changes turn into harm.
Looking ahead
All of this complex technology must survive in one of the harshest environments a device can be placed into: the modern hospital network. Ransomware crews don’t typically target specific devices; they target any vulnerability they can find. Flat or poorly segmented networks, legacy systems, and a patchwork of vendors create a huge attack surface.
As AI-enabled devices become more interconnected, a compromise in one corner of the network can have cascading effects. Hospital Chief Information Security Officers (CISOs) and procurement teams will push harder for evidence that AI-driven devices can survive in this reality, not just in a lab.
But none of these security efforts will be successful without collaboration. No single manufacturer, regulator, or hospital can solve AI safety and cybersecurity alone. The most credible AI-enabled devices will be those built with early regulatory engagement, honest dialogue with hospital security and clinical teams, and transparency about what the AI can and cannot do. That means being clear about training data, limitations, monitoring plans, and how the system will be updated safely over time.
