A hospital does not need to lose every record in a cyberattack to lose the ability to care for patients properly. If staff cannot access imaging, laboratory results, medication systems or appointment schedules, routine clinical work becomes difficult. If a network dependency fails, services that seem unrelated can be disrupted at the same time.
That is why a short exchange at Web Summit 2026 deserves attention from healthcare leaders.
Matthew Prince, Cloudflare's co-founder and CEO, said the internet was entering a period in which severe security vulnerabilities were being discovered with unusual frequency. He cited nine major vulnerabilities over the preceding three weeks and predicted more alarming discoveries over the next two years. But he resisted the idea that AI was simply causing the problem. His argument was that new tools were exposing dangerous weaknesses that had already existed, allowing defenders to find and repair them.
Those figures and forecasts were Prince's assessment on stage, not independently verified measurements in this article. The larger point is easier to evaluate: AI may accelerate both the discovery of weaknesses and the effort required to defend complex digital systems.
Healthcare has little room for complacency during that transition.
Why healthcare is unusually exposed
A modern hospital is a network of networks. It may operate clinical records, diagnostic imaging archives, laboratory systems, patient portals, staff communications, pharmacy applications, connected medical devices and services supplied by outside organisations.
The technical estate is not uniform. Some components are modern cloud services. Others may be older systems that require specialised maintenance or cannot be upgraded quickly without disturbing clinical operations. In a complex institution, even identifying which supplier owns a particular dependency can take time.
This creates opportunities for attackers and operational headaches for defenders. A software component may be vulnerable even when the hospital did not consciously choose it, because it is bundled inside another product. A seemingly minor service outage can interrupt appointments, theatre preparation or communication with patients.
The European Union Agency for Cybersecurity, ENISA, has repeatedly described ransomware as a major health-sector threat. Its 2023 health-sector analysis found ransomware represented 54% of the incidents examined across its study period. A later European threat analysis also documented incidents in which attacks on German healthcare organisations led to postponed medical procedures.
Those findings are not evidence that the specific vulnerabilities Prince mentioned have affected hospitals. They establish why security weaknesses are a patient-care concern, not merely an IT issue.
Discovery is good. Exploitation is still dangerous
Prince used the analogy of dangers hidden in a house. Discovering them can make the situation appear worse, because the threats suddenly become visible. But identifying a weakness is also the first step towards removing it.
That argument has merit. A vulnerability that is not known to defenders may still be known to attackers. Better testing can uncover problems earlier, including defects that have existed in widely used software for years.
The difficulty is the interval between discovery, disclosure, patch development, testing and deployment. During that period, attackers may attempt to exploit newly public information. Organisations must decide how quickly to change production systems without creating new outages or breaking critical integrations.
Hospitals often cannot apply a patch to every device immediately. A medical device may need manufacturer validation. A clinical application may depend on a particular software version. A system may be too critical to restart during operating hours. The sensible response is not to abandon patching, but to understand which assets are exposed, what alternative controls exist and who can make emergency decisions.
For healthcare, the relevant measure is not merely how many vulnerabilities an AI tool can find. It is how quickly the organisation can reduce exposure without compromising clinical safety.
A realistic hospital response
The first requirement is visibility. A hospital should know the software and connected services on which essential clinical pathways depend. That includes third-party components, remote access points and medical devices, not just centrally managed laptops and servers.
The second is prioritisation. A severe vulnerability on an internet-facing system with access to sensitive clinical data may demand a different response from a similar software defect on an isolated test environment. Threat intelligence, known exploitation, network exposure and clinical criticality should influence the decision.
Third comes resilience. Healthcare organisations need ways to continue essential care when digital systems fail. That may involve carefully rehearsed downtime procedures, accessible medication information, alternative communication paths and clear responsibility for switching services to contingency mode. A backup plan that has never been practised is only a document.
Fourth is supplier accountability. Hospitals depend on vendors for software updates, incident notifications and sometimes direct access to systems. Procurement teams should ask how quickly vulnerabilities are reported, how patches are supported and what documentation is available about important third-party components.
Finally, there is staff communication. Security teams should explain what a particular incident means operationally. Clinicians need to know which tools are reliable, what to do if information is unavailable and when to escalate. Telling staff simply to “be vigilant” is not a substitute for a workable process.
Where AI can help defenders
AI may support code analysis, vulnerability triage, anomaly detection and the interpretation of large volumes of security information. These are plausible uses, but the benefits depend on accuracy, implementation and human review. A system that generates thousands of low-quality alerts can overwhelm a small security team as easily as it can improve visibility.
In a hospital, an effective tool should help answer practical questions. Which exposed assets are part of a patient-critical workflow? Which vendors provide patches? Which systems can be isolated without affecting care? What changed during the incident, and what is the safest path to recovery?
A useful AI system might assist with prioritising evidence and assembling an incident timeline. It should not be assumed to understand every clinical dependency without accurate local data and oversight.
There is also a dual-use problem. The techniques that help security researchers discover flaws may help attackers automate reconnaissance or develop more convincing malicious material. ENISA's 2026 threat assessment notes that emerging AI models are expected to support malicious operations.
The right conclusion is neither “AI will save cybersecurity” nor “AI makes cybersecurity impossible.” It is that defenders need stronger fundamentals while the capabilities available to both sides evolve.
A message for medical-device and HealthTech companies
Founders should treat security maintenance as part of delivering a healthcare product, not as an obligation that ends at launch. If a device or platform is designed to stay in service for years, customers need to understand how updates, vulnerabilities and product dependencies will be managed during that period.
A company may invest heavily in clinical validation while giving little thought to the operational life of its software. Yet a clinically valuable tool that becomes unsafe or unavailable because of poor maintenance can undermine the care it was designed to improve.
This should shape architecture and commercial commitments from the beginning. Keep accurate asset and dependency records. Limit unnecessary access. Make logs usable. Establish incident-response responsibilities. Test how services recover from a failure. Give healthcare buyers clear answers about support arrangements and product lifecycles.
These are not glamorous differentiators, but they matter to the people responsible for keeping a hospital operating.
The difficult two years ahead
Prince's prediction was optimistic about the longer term. As more weaknesses are discovered and repaired, he believes the internet could become more secure despite an alarming flow of incident headlines in the near future. It is an interesting hypothesis, not a guarantee.
For hospitals, optimism about future security does not reduce today's duty to protect essential services. A useful way to read his remarks is as a call to prepare for faster discovery, faster disclosure and a more demanding remediation cycle.
The healthcare sector should welcome technologies that reveal hidden risks. But it has to build the capacity to respond before a vulnerability becomes an interruption to patient care.
In a hospital, cybersecurity is ultimately measured by continuity: whether clinicians can access what they need, whether patients can receive appropriate treatment and whether the organisation can recover when something fails. Every new AI capability should be judged against that standard.
Key Signals
AI is accelerating vulnerability discovery, and the window between a flaw being found and a hospital being protected is where the risk lives. Healthcare's exposure is operational as much as financial: when imaging, laboratory or medication systems go offline, routine care slows and error risk rises, as ENISA's threat reporting keeps documenting. Preparation means tested downtime procedures and segmented dependencies, not just better perimeter tools.
