How AI is Helping to Improve Healthcare Cybersecurity

Cyber Security

Healthcare organizations are relying on AI to help them improve their security posture. But, the technology must be implemented and maintained to ensure its effectiveness.

The benefits of AI in the cybersecurity field are evident. For instance, it can reduce medical errors and help to identify problems sooner. But it also comes with some downsides.

Detecting Malware

AI is helping healthcare cybersecurity professionals detect malware and other malicious software by identifying emerging attacks using patterns learned over time. While it’s important to note that these defense strategies aren’t foolproof and may be used by cybercriminals, they can expedite the response time for hospitals and health systems and prevent data breaches.

Malware can take many forms, from a virus that corrupts critical OS files to a botnet to launch large-scale distributed denial of service (DDOS) attacks. In addition, a hacker might use malware to extort money from an organization in exchange for a key piece of information, such as patient information.

Most malware aims to gain unauthorized access to the target’s data or computer system. In extreme cases, the hacker could even try to steal credit card numbers or Social Security numbers.

Some AI techniques work in a flow that includes data processing, feature selection, classifier training and malware detection. This improves the prediction model and gives a higher confidence level in identifying a malicious file.

Bad-behavior models include information about hosts and IP addresses known to be malicious and about traffic and data flows. When an unusual connection or data transfer is compared against these models, a match indicates with near certainty that the behavior is malicious.

Cyber Security

Detecting Phishing Attacks

Phishing attacks are one of the fastest-growing cybercrime threats, and the industry has turned to artificial intelligence for assistance in detecting them. These attacks are designed to target individuals and steal their data, including passwords, credit card numbers, Social Security numbers, and other sensitive information.

AI can help healthcare cybersecurity detect phishing emails, malware links, and other malicious software in real time. It can also identify suspicious domains and link patterns that suggest the phishing attack may be a scam.

In addition, AI can also help detect phishing attacks that target healthcare facilities and other sectors. This is because the medical industry has recently become a popular target for hackers who use phishing schemes to compromise the email accounts of healthcare professionals and unauthorized access to patients’ data.

This can result in massive breaches of healthcare records. Phishing attacks have been identified as the most common breach method in the 2020 Report on Global Threats.

Despite this, many security professionals believe the industry needs to be fully prepared to address the escalating phishing volume that will only continue to grow. Until now, phishing security solutions have been mainly reactive, using malware scanning technologies and domain reputation scores to block phishing emails.

This has increased the need for specialized and powerful AI anti-phishing weaponry. This technology can scour hundreds of thousands of phishing emails and alert on anomalies like lateral movement, lateral encryption, suspicious links, phishing signatures, and other behaviors that can indicate a phishing attack. It can then surgically neutralize those attacks before they infect more victims.

Detecting Intruders

The healthcare industry handles a lot of sensitive data daily. This includes patients’ medical records, doctors’ names, and appointment history. Keeping that information safe is no easy feat; this is where AI comes into the picture.

Security experts have found that AI is a good tool for detecting intruders and malicious software. It’s able to detect intruders through advanced analytics and algorithms. For example, suppose an AI-powered security system learns that a particular vehicle drives down a specific street at certain times of the day. In that case, it can use geo-enabled analytics to direct pan-tilt-zoom (PTZ) cameras in the area where the car is seen to zoom in and follow the intruder until it can be spotted and mitigated.

Similarly, it can use the camera’s facial recognition capability to spot faces and send alerts when someone is not in line with their usual routine. The AI system may have learned that someone typically comes home at a particular time and will recognize when that doesn’t happen.

In addition to spotting intruders, AI can also be used to flag other types of malicious software that can cause damage to the health system. This includes phony logins and other unauthorized data transfers. It can also be used to detect breaches of written policies, such as employees needing to follow procedures or completing required training.

Detecting Fraud

Detecting fraud is essential to cybersecurity since banks and other financial institutions rely on customers to trust them with their money. They can provide people with the funding to start and grow their businesses.

Banks now use AI and ML to automate payment processes and spot real-time suspicious transactions. This helps them to avoid fraudulent activity and reduce customer service costs.

Fraud detection has traditionally relied on rules-based systems, which are often convoluted and difficult to evade. They miss many fraud activities or create exorbitant estimates of bogus up-sides, where clients’ cards are declined because of misidentified and dubious behaviors.

Modern anti-fraud ML algorithms are better equipped to spot fraud because they can use vast quantities of accurate payment data. They can also identify and cut through noisy data to accurately classify fraudulent anomalies.

There are a variety of challenges that come with the implementation of AI in healthcare cybersecurity. These include procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and ensuring AI applications are ethically responsible. These issues must be addressed early in development to ensure that anti-fraud AI tools are implemented safely and effectively.