The healthcare industry has undergone a robust technological transformation over the last several years. More hospitals and clinical settings are equipped with advanced computers, servers, and artificial intelligence (AI) technologies than ever before.
AI is the latest technology to integrate into modern healthcare. At first, AI primarily assisted doctors in diagnosing patient conditions and conducting complex surgeries and procedures. Today, medical professionals rely on AI to enhance patient safety and security by protecting sensitive data from unauthorized third-party access. AI can also help prevent cyberattacks on computer systems and medical devices.
Why AI Security Has Become a Healthcare Priority
Traditional security in a healthcare setting meant locking paper files in a filing cabinet inside a secure room with a thick metal door. But since the digital revolution, medical professionals have found it more efficient to store patient records as digital files on computer servers, saving time and space.
Unfortunately, the biggest downside of digital file storage is the risk of hackers accessing healthcare data from remote locations. If the hackers are successful, they will have access to each patient’s name, address, contact information, medical history, social security number, and insurance details. Once hackers steal this information, they typically use it to commit identity theft. That can ruin a patient’s life for many years.
AI security has become the best defense against these cyberattacks targeting healthcare data systems. Thousands of hospitals and healthcare facilities worldwide have prioritized AI security to protect their patients’ sensitive data. AI algorithms can detect and block common cybersecurity threats to healthcare systems before they cause damage to the systems or patients.
The Expanding Digital Attack Surface in Modern Healthcare
Modern healthcare revolves around computer systems, robotic technologies, and digital recordkeeping. Because of this, it opens more avenues for hackers to infiltrate these systems and technologies, access patient data, and control the medical devices and equipment used to treat them.
None of this would have been possible a decade ago, when the average clinical setting had only a few computers connected to a server. Now, almost everything you see in a medical facility connects to a server, from heart monitors to surgical devices. While this may make it easier for medical professionals to treat their patients, it still expands the opportunities for cybercriminals to conduct digital attacks on healthcare facilities.
Limitations of Traditional Cybersecurity in Clinical Environments
Traditional cybersecurity solutions, such as anti-virus software and firewalls, offer limited protection against the modern cybersecurity threats facing clinical environments today. It is not enough to block viruses and malware on websites. Clinical environments must now protect their medical systems and equipment from hackers attempting to access them.
Too many hospitals and medical facilities still use legacy systems that rely on outdated hardware and software, which are no longer sufficient to protect against modern cybersecurity threats. Hackers can easily access patient data through these older systems due to the poor safeguards in place. The only way clinical environments can protect data on their legacy systems, aside from replacing them, is to use the latest AI security tools.
How Artificial Intelligence Is Transforming Healthcare Cybersecurity
AI has become more than just a precision tool for conducting surgeries and medical procedures. It is also transforming the way in which clinical environments protect their healthcare systems.
For instance, a modern hospital or clinical network has hundreds, and sometimes even thousands, of potential entry points that hackers can exploit to access the healthcare systems. There is no way that human staff members or traditional cybersecurity software can monitor each entry point for suspicious activity.
On the other hand, modern AI-driven security technology can automatically monitor every action on a clinical network in real time. AI algorithms can easily detect suspicious network activity, especially when an external computer attempts to access internal clinical systems. Once it recognizes bad behavior, the AI can take immediate action against threats without waiting for a human to act.
Azhar Ushmani’s Perspective: Addressing Generative AI Risks and Zero-Trust Security in Healthcare
Azhar Ushmani, a senior AI security engineer, has extensive experience in building secure AI systems and machine learning-enhanced cybersecurity solutions for healthcare facilities. He recently published articles in medical journals, in which he addressed the risks of generative AI and zero-trust security in healthcare.
Generative AI, which creates new content from existing data, is powerful enough to detect cyberthreats and enhance healthcare system defenses against them. However, there is also a risk that a hacker could manipulate generative AI to generate incorrect clinical information, leak sensitive data, or execute unauthorized actions against patients.
Ushmani discusses how the zero-trust security model can help reduce these risks posed by generative AI. Zero-trust security operates on the principle that no internal or external entity can be trusted when attempting to access healthcare systems. It doesn’t matter if it’s the chief of staff of a hospital or an anonymous person on the outside. If someone tries to access data within a system, the AI automatically assumes it is a data breach unless the person verifies their identity each time they access the data.
Data Protection, Privacy, and Regulatory Compliance in AI-Enabled Healthcare
The federal government has strict rules about how healthcare facilities handle patient data. Every doctor and medical professional is obligated to protect their patients’ data and identity. They are not allowed to share a patient’s medical records with anyone else without that patient’s permission. So, if a hacker were to access a patient’s medical records through a hospital computer system, that hospital would be held legally responsible.
AI-enabled healthcare security has become one of the most effective ways for hospitals and other clinical settings to comply with government regulations, protect patient data, and ensure their privacy. It ensures that hackers cannot access any patient data within a healthcare computer system.
Supporting Technologies: Federated Learning, Blockchain, and Secure Cloud AI
AI receives support from three key technologies: federated learning, blockchain, and secure cloud AI.
Federated learning eliminates the need to gather data in one central location to train AI models. Instead, a local AI device can access data from other places to train the model, making it harder for hackers to track it.
Blockchains, which are decentralized networks best known for storing cryptocurrency data, are also used in healthcare to create digital ledgers that store medical records. The best part is that only the recordholder can access their records.
Secure cloud AI is a secure cloud network with advanced encryption capabilities. Clinical facilities can use secure cloud AI to store encrypted medical data without managing their own computer systems.
Industry Adoption: Benefits, Barriers, and Implementation Strategies
Even though many healthcare facilities have implemented AI security technologies to protect themselves and their patients from cyber threats and data breaches, far too many still lack such protections. There continue to be barriers that make it difficult to implement AI security in their facilities.
One of those barriers is the cost of the advanced AI security technologies. Many small healthcare facilities, especially in rural environments, don’t have the budget to acquire advanced AI security. They also lack staff with the necessary cybersecurity skills and expertise to implement and manage AI security in their healthcare systems.
More public awareness of the importance of AI security might help overcome these barriers. Perhaps more government agencies can provide funding to clinical facilities to purchase and implement AI security into their systems. Then, the staff can receive training to ensure they know how to operate it correctly.
Market Growth and Future Outlook for AI Security in Healthcare
The evolution of AI has impacted almost every industry, and healthcare is no exception. We are merely seeing the beginning of AI’s impact on the security systems of healthcare facilities in the United States and throughout the world. As cyberattacks against healthcare facilities increase, greater investment in AI security technology will help ensure hospitals and clinical environments remain protected from hackers.
The future outlook is clear. AI security will become the standard in protecting healthcare systems worldwide. Patients can have more confidence that their medical data will remain protected. They will no longer fear hackers stealing it.
Building Trustworthy and Resilient AI-Powered Healthcare Systems
To prepare for market growth and the demand for AI security in the healthcare industry, we must build trustworthy, resilient AI-powered healthcare systems. It is the only way to move beyond the limitations of traditional cybersecurity and utilize the AI tools needed to protect against modern cyberattacks.
Everything from AI-powered system monitoring to blockchain verification and Zero Trust security can keep a healthcare system safe from hackers in the 21st century. It will be a difficult journey to implement these technologies across every healthcare system, but it is a worthy endeavor that will yield positive outcomes for patients and the healthcare industry as a whole.
