It’s no secret that the healthcare industry has a fraught relationship with cybersecurity. Despite being highly regulated, healthcare companies are hot targets for hackers. The wealth of patient data healthcare companies often possess sells for a premium on the dark web, and hackers have an opportunity to yield high ransom payouts due to the criticality of healthcare systems and services. After all, lives may truly be at stake amid a healthcare breach.
Historically, healthcare companies are also seen as easy targets because of outdated technology and systems. It’s not that upgrades aren’t a priority—it’s just extremely difficult to upgrade systems in an environment where you can’t risk any downtime of critical services.
The proliferation of AI is only making things more complicated. Healthcare companies must balance AI innovation with risk mitigation to provide the best solutions and care without compromising security.
Balancing innovation and risk management
The potential applications of AI in the healthcare industry are endless and exciting. It’s not hard to imagine a world where AI can enhance our ability to
build medical devices, increase the speed at which we bring new vaccines to market, or help analyze large amounts of health data to predict disease outbreaks. On the clinical side, AI can significantly reduce the burden of documentation, analysis, and diagnosis, giving medical professionals more time to devote to patients.
Across healthtech companies, hospital systems, research facilities, and more, there is a need to adopt, understand, and innovate with AI to push the industry forward. But to do so, organizations must ensure they have a strong security foundation in place—so innovation doesn’t increase the (already extensive) risk profile that healthcare companies carry.
Here are five practical tips to get started:
Understand your risk landscape
A big security mistake a lot of companies make is trying to bite off more than they can chew. They attempt to implement security controls across their whole environment, without a strong understanding of what they are actually trying to protect. This is especially detrimental for organizations with limited technical or financial resources, which is often the case in the healthcare industry.
The best approach is to first isolate and understand what you’re trying to protect and why—are you concerned about patient privacy, AI integrity, or something else entirely? This will dictate where you should focus the bulk of resources