Warakorn - Fotolia
As the new year draws near, healthcare organizations are thinking about where to focus their resources. Matt Mellen, security architect and healthcare solution lead at Palo Alto Networks, predicts that, in 2018, machine learning capabilities will not only enhance a healthcare organization's cybersecurity program, but improve patient outcomes as well.
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
Why is now the time for healthcare organizations to consider applying machine learning capabilities to cybersecurity?
Matt Mellen: Healthcare has seen more than its fair share of cyberattacks for a variety of reasons and it urgently needed a game-changing security technology to prevent them. I think machine learning is that game changer, and it's going to have a pretty significant impact on [the ability of healthcare organizations] to protect themselves from cyberattacks, cyberbreaches, at the same time improving healthcare practitioners' ability to provide highly accurate diagnoses. The key in making machine learning algorithms that work properly is having a lot of data to feed into the algorithm. The more data, the better; the more data, the more accurate the machine learning algorithm result.
In healthcare, I know that hospital networks are building massive data lakes to store all their health information with the intent on having it evaluated by machine learning algorithms and hopefully result in the ability to provide better diagnosis. But in cybersecurity the winners are going to be -- and by winners, I mean the security tools that will be the most effective -- those that will have a significant amount of threat data to feed into their machine learning algorithms.
Machine learning is clearly going to have a growing impact on the effectiveness of cyberattack prevention and beyond just medical diagnoses to other areas of the field like predictive analytics, which is predicting outcomes before they happen, using natural language processing to extract meaning out of images, which is a real challenge in healthcare, because, for example, radiology images are not easily searched or digested by software.
Matt Mellensecurity architect and healthcare solution lead, Palo Alto Networks
My recommendation is for CISOs of healthcare organizations to start planning to adopt machine learning capabilities in their cybersecurity programs, and to specifically look for security products that have machine learning based on large data sets and ensure that they have consistent cyberattack coverage across the end points, the network and in the cloud.
What kind of investment will healthcare organizations have to make to apply machine learning capabilities to their cybersecurity programs?
Mellen: It really depends on the size of the organization. ... But what I typically recommend is focusing on a phased approach to most problems. A lot of healthcare organizations first focus on the edge, protect the edge of their network, figure out the ingress and egress points to their network and protect those first. And you can do that with a next-generation firewall. ... It does not require a significant amount of change to the environment.
Do you see cyberattacks continuing to be a threat in 2018?
Mellen: Ransomware is definitely going to continue given that it is the most effective and quickest way for attackers to monetize their efforts and not get caught. If you end up exfiltrating or stealing protected health information out of healthcare organizations, you have to figure out how to sell it. And when you do that and you go into the dark web to sell it, there's a higher risk of getting caught by the authorities. Hence, most attackers continue to just widely use ransomware to make money and not get caught.