For the past few years, the number of attacks on networked medical devices has been on the rise, and there’s reason to believe they’ll only become more frequent in the future. Medical device manufacturers will need to take proactive steps if they want to defend patient data.

Cybersecurity threat prediction refers to a range of cybersecurity platforms that use technology like AI to proactively monitor for cyberthreats. The approach could be an essential tool for manufacturers wanting to keep devices safe.

The Challenges of Cybersecurity in Healthcare

Right now, medical device manufacturers face two major cybersecurity challenges.

The first is that the structure of modern networks increasingly nudges the odds in hackers’ favor. A growing number of networked medical devices and larger global systems often makes it harder to effectively defend against attacks. A smart machine, the network it is connected to, and its applications can all provide an attack surface for hackers.

The unique nature of some medical devices can also make them particularly hard to keep secure. Hospitals may have little room for downtime in equipment like smart patient monitors, which can make following a manufacturer patching schedule difficult. Implanted machines may be difficult or expensive to replace, making vulnerabilities in hardware or firmware much more challenging to manage.

The second challenge is a talent issue. Cybersecurity professionals are in short supply, and the industry has struggled to build an effective talent pipeline that can supply businesses with the experts they need. This talent gap is likely to last well into the future.

At the same time, those professionals currently in the field are under growing pressure to manage the rise in cyberattacks with limited resources. It’s not uncommon for cybersecurity teams to struggle with burnout as a result.

While the typical solutions to cyberattacks are still effective — like encryption and network monitoring — sometimes they aren’t enough.

More often, businesses are solving these challenges with automated solutions. They often help teams do more with their resources — improving efficiency and reducing the workload on cybersecurity professionals.

Cybersecurity Threat Prediction for Medical Devices

Medical device manufacturers looking for automated solutions in device security have considered one of the many ways AI is reshaping the health care industry — such as predicting cybersecurity threats. Such solutions use AI- or big data-enabled predictive analytics. Data gathered from the network, combined with historical security information, is analyzed by an algorithm that calculates the likelihood of future network events, like attempted breaches.

At the same time, the user may apply cybersecurity tactics like the use of backups, a proactive measure that helps ensure critical data isn’t completely destroyed, even if an attack is successful.

This combination of tactics is different from some conventional cybersecurity approaches, which often involve after-the-fact responses to threats.

Once an attempted attack is detected, experts work to find out where it came from and how to prevent others like it in the future. Much of this defense is signature-based, meaning cybersecurity workers or on-device security technology will look for combinations of data that are unique to particular attacks.

Threat prediction goes one step further by using predictive analytics to proactively scan the network for vulnerabilities and attack vectors before hackers can target them.

The same algorithms continue to gather network data over time after they are deployed. This allows them to build a kind of self-learning system that can continuously develop new analyses on network vulnerabilities and hacker tactics. It also enables cybersecurity teams to go beyond signature-based defenses, allowing them to defend against attacks with unique signatures.

In practice, this tech has already been used for DDoS attack detection in IoT devices. Experimental models found that the approach can detect these attacks with an accuracy rate of 97.16%.

These predictive algorithms could provide medical device manufacturers with protection and a valuable source of information on hacker behavior. Over time, this data could enable the development of more-effective on-device security technology, allowing companies to stay one step ahead of attackers looking to access patient medical data.

Using Threat Prediction to Defend Patient Data

The growing threat of cyberattacks means medical devices may become more difficult to protect over the next few years.

Because reactive cybersecurity strategies are becoming less workable, automated and proactive methods may become essential to medical device manufacturers. Approaches like threat prediction cybersecurity can help them prepare for attacks before they happen, reducing the risk of breaches and unauthorized access of patient data.