In the healthcare sector, the Internet of Things (IoT) offers a plethora of benefits, ranging from monitoring patients more closely to using data to gain insights on patient care – and the usage of these connected devices is growing. The global medical device market is expected to reach an estimated $409.5 billion by 20231, and it is forecast to grow at a CAGR of 4.5% from 2018 to 2023. But unfortunately, the rise of IoT has also led to the rise inanimate devices being used to hack systems, which can have a catastrophic effect. With the use of IoT-based monitoring equipment, such as devices that dispense pulmonary medication, hacking can kill.
This issue is not solely at the hands of the manufacturers of these devices. Many clinics and hospitals need to reconsider how they are supervising, managing and protecting this technology – by connecting it to hospital networks as securely as possible.
Today, there are still reports of unpatched devices, unencrypted data traffic, and an unproductive disconnection between the tasks performed by IT teams who are responsible for information systems, and clinical engineering teams who directly manage medical devices. These concerns are of course escalated in an industry where data is highly personal and there is much at stake with an invasion of sensitive patient information
One of the biggest challenges for organizations across the board is a significant shortage of experienced employees in cybersecurity. According to Cybersecurity Ventures, recent estimates show that there will be as many as 3.5 million unfilled positions in the industry by 20212.
Most of major breaches have taken place through a third party performing a service for a separate organization. When businesses hire third parties for financial matters, they can unintentionally become a target for fraud.
However, by leveraging machine learning, organizations can largely diminish the security concerns that come with Internet of Things (IoT). Machine learning enables data exchanges to be monitored within the organization, and with external parties to detect anomalies that are not considered the norm. Machine learning can also aid in predicting threats, in its ability to analyze historical data from specific trends, which can be evaluated from the big data produced by the algorithms.
Because machine learning can detect activity on the network and endpoints in real-time, the organization can be equipped with all-inclusive enterprise detection and protection technology, thus enabling the monitoring of activity across all devices. An important factor to note is that machine learning cannot rely on old signature-based tech, more commonly referred to as legacy systems. Failing to update back-end systems can produce a domino effect, as hackers now have tools that are much smarter than these dated systems. To protect themselves, companies need to couple behavioral analysis with supervised machinery – i.e., human monitoring of the technology. This can avoid the potentially disastrous consequences of a breach that goes unnoticed.
By integrating the proper cybersecurity measures, such as user behavior analytics, data loss prevention, and endpoint security technologies, an organization can better protect both its patient data and infrastructure from ransomware attacks. In implementing a system where the human point is guarded, and people interact with intellectual property and critical organization data, the intersection of users, data, and networks can be considered more effectively and the cyber threat protection across the healthcare industry can be largely enhanced.
Today, healthcare players can no longer afford to view cybersecurity as an afterthought in the budget planning process. Due to the magnitude of the ramifications that can occur from a lack of investment in cyber security, it should be at the forefront of business efforts, helping to protect from the destructive impact that a breach can have on any organization.