One of the largest threats that will continue to threaten the United States’ healthcare system through 2021 will be ransomware. Ransomware attacks have become exponentially more sophisticated in the last two years, and that trend will only continue.
One of the most considerable challenges in combating ransomware is the method of execution and delivery. An attacker can embed ransomware in an email, a file attachment, or even a text message. And even the market leaders in detection and prevention are still playing catchup. So much so that the strategy around ransomware has shifted from prevention to recovery.
The healthcare system is especially vulnerable. Due to COVID-19, there is an increased demand for all resources within the medical system. Emergency room staff, doctors, and nurses have even less capacity for critical decision-making when a malicious email comes their way. Attackers use strategies to strike at the precise time when someone is at their least attentive.
It is a fine line to walk between patient care and patient privacy. Given a choice, doctors and nurses will always choose patient care first. This offset is where data protection and deep learning systems can come into play.
Utilizing a combination of machine learning, AI, and deep learning systems can help prevent healthcare data from falling into the wrong hands. These systems can flag and retain a potential compromise of data before it becomes a breach.
Much like a credit card company can lock a credit card from being used in what is known as a “superman attack” or impossible travel.” This attack describes a credit card being used in two physical locations without the ability to travel between them in that amount of time.
However, these systems come at a premium cost to healthcare providers and may not be realistic in terms of resources. So while there is hope on the horizon, we still need to be diligent in strengthening the cybersecurity chain’s weakest link: the user.
