FREMONT, CA – Data privacy is a vital concern for every industry, especially healthcare. Hospitals have the responsibility to maintain patient records securely. Any breach in their electronic medical records (EMRs) can ruin their reputation and subject them to violation of regulations. Hence, it is crucial for healthcare organizations to have state-of-the-art cybersecurity systems to safeguard patient data.
The implementation of machine learning (ML) technology can assist the organizations in augmenting their cybersecurity systems, not only to protect the patient data, but also the network of computers in the organization. ML approaches can aid in fraud detection, anomaly detection, and predictive analysis to detect malware, viruses, and similar cybersecurity threats.
The EMRs are usually stored in the database of the healthcare organization, and can be accessed remotely from examination chambers during patient appointments for the doctor’s reference. The EMR is updated and saved in the database by the examining doctor after every evaluation of the patient. The databases usually contain crucial personal and organizational information such as EMRs, patient data, resource utilization information, tax and financial data, personal data of the employees, and so on.
The integration of ML-powered anomaly detection software into the overall network can assist the organization in analyzing network activity in real time. The ML model can study the normal functions and events in the system for its reference, and use it to detect any deviations. It will enable the software to protect all the computers in the network which often serve as gateways to the hacker.
The deviation from normal function might also urge some solutions to shut off the fraudulent user from the network. In case the flagged activity is deemed to be reasonable by the management, the ML model will take it into its future decision-making process.
The healthcare organizations can leverage ML in anomaly detection to locate the source of cyberattacks, as well as the nature of the attacks. It can monitor all the computers in the network for unusual activity, which could lead to malware detection.
Predictive analytics of ML can be used to predict the presence and behavior of malicious files in the network of the healthcare organization. Hence, the malicious files can be prevented from opening. It can help in the detection of other forms of malware as well.