The healthcare industry is one that processes a wealth of sensitive information on a daily basis. Just imagine it, from dental records and information on emergency procedures to other surgeries, patients may prefer to keep private - hospitals and medical clinics handle it all. It’s precisely for this reason that AI for cybersecurity in this sector is no longer a strategy employed only by industry leaders.
Apart from applying AI to uphold cybersecurity in government agencies, find out how it’s helping hospitals and clinics keep their patient data and networks safe through machine learning, natural language processing, and adaptive learning techniques.
ANOMALY DETECTION FOR MORE SECURE, PATIENT DATA STORAGE
If anomal detection sounded like a purely medical term, guess what? It’s also a major part of applications of AI for cybersecurity in the healthcare sector.
Through this type of software, IT and security teams can see where cyber-attacks may be originating from and what kind of attacks they are. Anomal detection allows administrators to monitor users within a given healthcare network and keep an eye out for threatening behaviour.
Without the need for round-the-clock monitoring, AI does the heavy-lifting, facilitating more accurate detection of malware and other threats. This, in turn, keeps sensitive medical information further out of reach from determined hackers - a big win for the medical industry.
AI PROVIDES SECURITY PROFESSIONALS WITH MORE DATA ON CYBER THREATS
Another amazing capability of AI for cybersecurity is its ability to visualize threats and provide more data to security teams than previously imagined.
While hospitals may require certain technologies like graphic user interfaces, this is a small price to pay for greater knowledge on the nature and scope of attacks levelled against healthcare facilities. Already, numerous medical institutions have had success with this type of AI-based cybersecurity software.
Through the comparison of real-time data with historical activity patterns, security professionals can detect certain patterns with regard to a healthcare facility’s vulnerabilities. Human agents can then train machine learning models on captured data from suspicious activity and automate threat-detection or even alert users when there are issues that require manual resolution.
PREDICTIVE ANALYTICS FOR THE PROTECTION OF HEALTHCARE NETWORKS
Predictive analysis is one of AI’s most touted fortes and in the context of cybersecurity for the healthcare sector, this kind of technology is being used to protect networks from rapidly-evolving malware.
Using principles of machine learning, this software is trained to analyse network activities, which are then labelled and classified as ‘normal’ or ‘regular’ activities that pose no risk. In the event that activities totally incongruous with these actions/behaviours arise, operators will know that it’s more likely to be a cyber-attack.
AI will provide this information to IT and security teams in real-time, making the monitoring process more convenient, accurate, and data-driven. This software may also be able to indicate whether bots or human agents are behind each attack.
Given that IT teams will be responsible for feeding data on what is classified as fraudulent or threatening activity, predictive analysis software of this nature will have the ability to recognise new types of malware attacks.
AI FOR CYBERSECURITY IN THE HEALTHCARE INDUSTRY IS MORE ESSENTIAL THAN YOU MAY THINK
In industries as vital as the healthcare industry, cybersecurity represents a major area of investment and activity given the potential fallouts of a successful cyber-attack.
In 2016 alone, the US healthcare industry was the subject of 88% of all ransomware attacks. Even in Australia, the healthcare industry is a prime target for malicious cyber activity.
It is apparent, therefore, that leveraging AI for cybersecurity in the healthcare industry is not a luxury or an option that is availed only by mega-sized facilities but one that the entire industry must flock to for improved data protection.
Given the nature of the information these facilities handle, store, and process, any data breach can have lasting and disastrous outcomes. In the absence of the requisite knowledge and skills to introduce AI cybersecurity strategies to healthcare facilities, turning to experts well-versed in this area is a foolproof move.
