MedCrypt, a medical device cybersecurity software provider, announces a $5.3 million Series A funding round led by Section 32, with participation from Eniac Ventures and Y Combinator. MedCrypt was part of Y Combinator’s Winter 2019 batch.
Mike Kijewski
“Last October, the FDA released a major update to its premarket cybersecurity guidance for medical devices, publishing guidelines that line up just about perfectly with the solution we began developing three years ago,” said MedCrypt founder and CEO, Mike Kijewski. “Internet-connected medical technology is entering the market at light speed, calling for devices to be secure by design, which leads to a heightened level of patient safety at all times. We’re thrilled to see continued support from various groups in the industry, from the government to healthcare institutions and device vendors, along with support from our partners to help us further develop our technology and expand our team.”
The HIPAA Security Rule has been in effect for 14 years, aiming to protect electronic health data, yet a new study from CynergisTek reports the healthcare industry has only managed to achieve 72% compliance with it, leaving a gap that poses a security risk for those who are not yet compliant. The study also reports healthcare is expected to suffer two to three times more cyberattacks in 2019 than other industries. This data makes patient safety a critical area of focus.
“Patient data privacy has long been a concern, but the healthcare industry is just beginning to address patient safety risks presented by internet-connected healthcare technology,” said Vidya Murthy, vice president of operations, MedCrypt. “Research shows a 13.3 percent higher mortality rate for patients experiencing a cardiac arrest whose care was delayed by four minutes. While cybersecurity attacks to a device such as a pacemaker seem more dangerous, delays to patient care because of cyberattacks are much more real and likely.”
MedCrypt will use the funds to expand its team, adding new members in sales and engineering roles, and further develop its technology. MedCrypt’s security software allows device vendors to use cryptography to secure data traveling between or stored on devices. MedCrypt then provides remote, real-time monitoring to alert medical device vendors of suspicious behavior that may yield potential security threats to their company, devices and patients.
This round brings MedCrypt’s total funds raised to $8.4 million.
Advancements in medical device technology has allowed for services, initiatives and changes in healthcare delivery to evolve at a break-neck pace. Smartphones are increasingly integrated into patient care planning, providing internet connectivity to share data to healthcare delivery organizations (HDO), doctors and researchers. It is unfortunately also true that as the medical treatment landscape has evolved, it has been challenged by cyber-attacks. While shows like Homeland have portrayed the vice president’s wireless pacemaker introducing a vulnerability that can be used in an assassination attempt, individual patient harm is not the common scenario HDOs and patients face.
Instead, as a recent report from Positive Technologies indicates, healthcare hackers seem motivated to seek sensitive information and control over a system, compared to stealing financial information, or even money. How does this motivation impact a defense strategy in the already complicated healthcare ecosystem?
Location of care delivery
Let’s begin by understanding the volume of the situation. The average hospital bed has 10 to 15 devices connected to it. With the American Hospital Association count of hospital beds above 6,000 in 2019, this is in the frame of 900,000 devices inside U.S. hospitals. These devices often have Bluetooth or wireless capabilities. An adverse player in the ecosystem can potentially exploit this connectivity with the intention to expand into the HDO network, hospital/device database or elsewhere.
Healthcare has been shifting outside of the HDO to accommodate increasing costs in care delivery, remote patient geography and to accommodate populations that are unable to access an HDO on an ongoing basis. These changes have been great for patients and providers, enabling ongoing monitoring of patients even when they’re not in the HDO. But it also means that some connected devices operate outside of the secured and monitored HDO network, while sending data back to providers within the HDO network. The introduction of these connection points also serve as the introduction of additional threat vectors that need to be managed.
Types of data available
It’s not immediately obvious what data used in clinical care could be used by hackers to elicit monetary benefit for themselves. The idea of a blood pressure or ECG reading doesn’t exactly bring dollar signs to mind.
HDOs and care providers regularly obtain patient social security numbers (SSN), which can be relevant for billing purposes, or in an attempt to share data between HDO systems. This same data can be used by a malicious actor to commit requests for loans, prescriptions or insurance claims, open bank accounts, perform online transactions and even file taxes or claim rebates. Imagine the SSNs from a pediatrician’s office being sold and the fraudulent activity going undetected for a prolonged period, or the SSN of a deceased person that can be used with zero concern for active monitoring by the individual.
Records can also include communication methods for patients, such as email and phone numbers, which can be used for spreading spam/malware with the intention of running phishing campaigns. This is to say nothing of personal distress that can be introduced if patient medical conditions are known by individuals without the patient’s best interest in mind.
Individuals who use commercial trackers to identify fitness patterns and metrics to discuss with providers have intentions of bringing more data to a potentially difficult diagnostics. However they are also capturing information that can be correlated to determine physical location. The army base location that was disclosed because of GPS-related workout data demonstrates how different types of information can appear unrelated, yet end up unintentionally giving something crucial away.
