As businesses big and small across the healthcare industry become increasingly reliant on technology to optimize and deliver patient care, effective managed cybersecurity services (outsourced management of security procedures and systems) have never been more important. Hospitals, clinics, and other healthcare organizations are regularly targeted by cybercriminals attempting to steal sensitive patient data or disrupt critical operations.
To better defend themselves, healthcare organizations are turning to companies like Derive Technologies to create cybersecurity strategies that can keep critical patient data safe and safeguard IT operations.
An area in which companies like Derive can make significant contributions is in implementing healthcare cybersecurity solutions that leverage the unique power of language learning models and generative AI. This blog introduces these brand-new, groundbreaking technologies that already are being utilized to improve managed cybersecurity services and protect patient data.
Before we dive into specific applications of language learning models and generative AI–both of which are just hitting mainstream conversations–it’s important to understand what they are and exactly what they do.
Language learning models refer to machine learning algorithms designed to understand and analyze human language. These models are trained on vast datasets of both written and spoken language, allowing them to recognize patterns, identify key themes, and generate human-like responses.
Generative AI, on the other hand, refers to AI systems that can create new content or data by synthesizing existing information. Although today’s headlines stay largely focussed on search engine capabilities, this technology is already breaking ground in healthcare cybersecurity as well, where it’s being used to simulate attacks, identify vulnerabilities, and much more.
One of the most significant challenges facing healthcare cybersecurity is the sheer volume of data that must be analyzed to identify potential threats. IT companies like Derive Technologies are employing solutions that use language learning models to process and analyze massive amounts of text-based data, including emails and social media posts to other forms of digital communication.
By analyzing these data troves, language learning models are able to recognize patterns and anomalies that may indicate a looming cyber attack.
A model trained on the language used in phishing emails, for example, could help identify similar emails that might be attempting to steal sensitive patient data.
Language learning models can also simulate a phishing attack, allowing security teams to identify chinks in the armor of their email security protocols (which remains the biggest and most regularly exploited threat vector). By identifying and addressing these vulnerabilities, healthcare organizations can help ensure that any attempts at cybercrime are unsuccessful.
Another way that language learning models improve healthcare cybersecurity is by providing more effective threat intelligence. Thanks to their ability to analyze vast amounts of online data, language learning models can identify emerging threats such as new malware or phishing techniques, which in turn directly informs an organization’s security protocols and defenses.
In addition to language learning models, IT companies like Derive are also implementing solutions that use generative AI to improve healthcare cybersecurity.
Generative AI can detect irregularities in network traffic and learn to identify suspicious patterns in an organization’s internal network(s) based on the synthesis of massive datasets.
A generative AI system trained to recognize normal patterns of data flow in a hospital network, for example, will instantly detect when a new and abnormal pattern emerges. This is often an early red flag that a cybercriminal has gained access to the network, with a likely intent to steal patient data or disrupt critical healthcare services.
By detecting and responding to attacks in real-time, generative AI is already being leveraged by companies like Derive to help protect healthcare organizations against ransomware. Ransomware attacks involve cybercriminals encrypting a healthcare organization's data and demanding payment in exchange for the decryption key.
Generative AI trained on historical data from past ransomware attacks is able to quickly analyze the code used by the attackers, the methods they use to distribute the ransomware, and the types of files and data that are targeted to identify patterns and characteristics of ransomware attacks.
With a wealth of data knowledge, generative AI can genuinely detect and respond to ransomware attacks in real-time. For example, if a generative AI system detects a suspicious file or piece of code entering the network, it can isolate and quarantine the file before it has a chance to spread and encrypt data across the network.
Lastly, generative AI is able to quickly generate fresh security protocols and updates in response to new ransomware attacks. These are just a few of the exciting ways this technological leap is already helping healthcare organizations stay one step ahead of cybercriminals.
Conclusion
As healthcare organizations continue to adopt new technologies and face emerging threats, the need for effective cybersecurity services has never been greater. IT companies like Derive Technologies are playing an increasingly critical role in protecting patient data and securing critical healthcare systems. By leveraging language learning models and generative AI, Derive is able to provide stronger, more future-proofed managed cybersecurity services for healthcare clients in the greater NYC area.
