Introduction
The healthcare industry is undergoing a digital transformation. As medical technology becomes more advanced and the internet of things (IoT) continues to infiltrate patient care centers, hospitals must become more sophisticated in their cybersecurity efforts. The stakes are high: a breach could affect thousands of people at once and have long-term implications for those affected by it. That's why understanding these 10 key cybersecurity terms is so important—it's vital that healthcare leaders understand how they work, what they mean, and how they can help protect their organization from cyberattacks:
1. Zero trust architecture
Zero trust architecture is a security model that assumes that all users, devices, and applications are untrusted. In other words, it's a shift from the traditional "trust but verify" approach to security.
Traditionally, IT staff has been concerned with keeping unauthorized people out of your network rather than verifying who they are or what they can do once inside. While this approach is necessary for highly sensitive data sets (e.g., credit card numbers), it doesn't work when applied to everything in an organization because it slows things down too much—and you don't want your employees waiting for their computers to boot up every morning!
Zero trust is about letting users access just enough information as needed without giving them full access rights before they've proven themselves trustworthy by doing something useful (or beneficial) within your network environment. This makes sense for healthcare organizations where sensitive patient information may be available across many different departments: If one department wants access to an individual record but does not need full access rights yet (e.g., just their name), then the system grants only those limited permissions until there's sufficient evidence that this person should be trusted with more power over other users' personal data (such as health records).
2. Tokenization
Tokenization is the process of replacing sensitive data with non-sensitive tokens. This is done to protect personal information at rest, in motion and in use.
It's important for healthcare organizations to understand tokenization because it's used to secure personal health information (PHI) stored on servers as well as data sent over networks or transmitted by mobile devices, such as mobile apps and wearables — essentially anything that connects to a network.
Tokenization can be applied to all forms of data: contact information; medical records; patient names; even Social Security numbers -- all without compromising the integrity or usability of any piece of information.
3. Deceptive technology
Deceptive technology is a type of malware or malicious code that uses human interaction to compromise systems. This can be done by tricking the user into clicking on something they should not, or stealing information from them without their knowledge. Examples of this include fake antivirus software and websites designed to lure in users with false promises.
The problem with deceptive technology is that it can be difficult for most users to distinguish between real and fake websites, resulting in their becoming infected with malware without even realizing it until it's too late. Deceptive technology can also take advantage of poor security in healthcare systems, so if you're not protecting yourself against these types of attacks already, your system could become compromised very quickly!
To protect yourself against deceptive technology (and other forms of cyber-attacks), make sure that all computers connected directly or indirectly with patient care have access only through an internal network filter which prevents outside access and blocks any known viruses before they get inside the network itself.
4. AI/ML-based security and analytics
AI/ML-based security and analytics are used to detect, classify, and respond to threats. AI/ML can be used for anomaly detection, malware detection, threat hunting, data loss prevention (DLP) rules, and more.
AI/ML has been around for some time now but has not yet lived up to its promise as an end-to-end solution for security automation. It's still early days for AI/ML applied across the healthcare enterprise; however, we're seeing it being used in new ways by organizations like IBM with their Watson Health platform or Amazon Web Services (AWS) with its Healthcare Cloud offering.
5. Multi-factor authentication
Multi-factor authentication (MFA) is a method of protecting your accounts by requiring more than one factor to complete a login. Examples include:
- A password and personal identification number (PIN). This is the most common type of account protection, but it can be easy for hackers to guess or steal from you through phishing emails or other methods.
- An email sent to you with a code that needs to be entered after you sign in. If your email provider doesn't have good security, hackers could get into your account this way as well.
- A phone call requesting OTPs (one-time passwords), which are numbers sent via text message. While this option may seem inconvenient, it's still more secure than using only email verification because it requires another piece of information from you instead of just knowing your username and password like the other two methods do.
6. Privilege access management
Privilege access management is a set of guidelines and policies that give employees access to certain systems and tools based on their role in the organization. For example, different levels of IT staff might have different permissions for accessing patient records or computer systems. This can help prevent insider threats by making sure all employees only have the level of access needed for their job tasks.
Privilege access management also helps protect patient privacy by ensuring that only authorized users are able to view patient records. A healthcare organization may want its physicians to be able to make edits in patients’ files, but it doesn't want them seeing each other's notes—or anyone else's notes, for that matter!
7. Behavioral analytics
Behavioral analytics is a form of machine learning that uses artificial intelligence (AI) to detect abnormal behavior within an organization. It can be used to identify potential cybersecurity threats and prevent them from occurring in the first place. For example, behavioral analytics systems may be able to identify users who use the same device for multiple logins every day or are accessing multiple accounts at once. These patterns of activity could indicate that a hacker has gained access to an employee's account credentials and is trying to access other systems or accounts on their own devices. The system can then automatically block these suspicious attempts before they become successful breaches.
8. Data classification and labeling
Data classification and labeling is a way to organize data so that it’s easier to find. Classification refers to the type of information in your organization’s records, including personal health information (PHI), financial information, protected health information (PHI), and more. Labeling is the process of identifying what type of data it is and how you want to categorize it for storage purposes.
9. Managed detection and response (MDR)
Managed detection and response (MDR) is a service that provides 24/7 monitoring of your network for potential threats. If you’re attacked, MDR can help you identify the source of the attack so it can be contained. It also provides services to help you recover from an attack, such as rebuilding servers or restoring data.
MDR typically uses sensors placed strategically around your networks to scan for signs of suspicious activity. This can include missing antivirus signatures, unusual traffic patterns, security settings that aren’t compliant with industry standards (like firewalls with default credentials), or other indications that hackers may have broken in.
10. Cryptography and key management
Cryptography, the science of writing in secret code, and key management, the process of managing cryptographic keys, are at the heart of security for data protection. Cryptographic keys are used to encrypt and decrypt data. They're also used to sign data so that it can be authenticated by a receiver who was given a public key from you.
Conclusion
As you can see, there are a lot of different ways to protect your data. It’s important that healthcare organizations find the right tools for their unique needs, whether that means using AI/ML-based security and analytics or implementing a zero-trust architecture.
In this article, we covered 10 cybersecurity terms healthcare leaders need to know so you can better understand how your organization can stay safe from cyberattacks and protect its most valuable asset—your patients’ data!
Medicai offers a safe and secure cloud-based infrastructure for medical imaging using the most recent cybersecurity technology. Cloud-native technologies powered by AWS ensure host and endpoint security, network and infrastructure security, data protection and encryption, vulnerability analysis, governance, risk management, and compliance.
