Security is crucial for all industries, but healthcare faces more pressure than most due to the vast amounts of highly sensitive information stored in hospitals, making them ideal targets for cybercrime. This has necessitated the use of extensive defenses, with user and entity behavioral analytics (UEBA) being one of the most helpful tools in that endeavor.
While the medical sector is no stranger to artificial intelligence, most medical AI applications focus on patient care or administrative work. However, applying it to cybersecurity in the form of UEBA is a crucial step forward.
What Is User and Entity Behavioral Analytics?
User and entity behavioral analytics utilize machine learning to detect threats such as breached accounts or ransomware. While protections like multi-factor authentication aim to prevent attacks, UEBA instead focuses on stopping threats that slip through the cracks before they can cause much damage.
UEBA analyzes how different users and entities behave on a network, establishing baselines for normal behavior, and uses machine learning tools to detect suspicious activity. This can include an account trying to access a database it rarely needs or downloading something at an odd time, flagging it as a potential breach.
This process is similar to how your bank may freeze your credit card if you make unusual purchases, but UEBA applies the concept to network behavior using AI to make it faster and more accurate.
UEBA Benefits
UEBA use cases have many benefits spanning multiple applications, some of their most significant ones are:
Accuracy
Behavioral analytics systems are highly accurate. Machine learning can pick up on trends and patterns in data that humans may miss, allowing UEBA tools to outperform human analysts when determining what is and isn’t suspicious. When properly applied, UEBA can also yield false positive rates as low as 3%, ensuring security teams don’t waste their time or resources.
UEBA can achieve higher accuracies than rule-based monitoring systems because it’s adaptive. Machine learning algorithms continually gather new data, adjusting their decision-making as trends shift, accounting for nuances like users slowly adopting new habits or activities being normal in some situations but not others.
Efficiency
Another benefit of UEBA is its speed. Machine learning tools can detect and classify anomalies almost instantly, whereas it may take a human a few minutes. Even if those time savings are just a few seconds, they can make a considerable difference when dealing with cyber threats.
UEBA tools can often detect suspicious behavior before an account or breached device causes any real damage, dramatically reducing the impact of an attack. IBM found reducing data breach response timelines saves organizations $1.12 million on average.
Versatility
UEBA is also versatile compared to similar security tools. Some organizations employ user behavior analytics (UBA), which provides similar benefits but only looks at user activity. By also including entities, UEBA expands its detection capabilities to IoT attacks and other hardware breaches, preventing a broader range of incidents.
Machine learning tools like UEBA are also more versatile than rule-based anomaly detection. AI models can adapt to changing situations and account for situational differences, a flexibility vital for healthcare organizations as telehealth has grown 38 times over its pre-COVID levels, meaning more medical staff may access systems from changing locations.
UEBA Use Cases in Healthcare
These benefits are impressive, but how much medical companies experience them depends on how they implement this technology. Here are the five best user and entity behavior analytics use cases in healthcare.
1. Automating Risk Management
Risk management automation is one of healthcare organizations’ most beneficial UEBA use cases. IT monitoring is crucial in this industry, but many businesses need more time or staff to manage it manually. UEBA reduces that burden by handling network threat detection without manual input. Hospitals don’t need large security teams to monitor their systems 24/7 because AI will do it for them.
Because UEBA is so accurate and efficient, medical staff can use electronic systems more efficiently. There will be fewer verification stops or run-ins because of false positives, helping reduce the burden of EHRs, improving both cybersecurity and patient care.
2. Detecting EHR Breaches
UEBA has many advantageous specific use cases under the automation umbrella, too. One of the most relevant for healthcare organizations is detecting and responding to breaches in EHR systems. Electronic records make it far easier to manage patient data, but they also introduce significant security risks. There were over 700 health record breaches of 500 records or more in 2022 alone, averaging almost two breaches daily. Given this issue’s common and severe nature, UEBA is an indispensable tool.
UEBA can recognize when an app or account is accessing an unusual amount of records or interacting with them atypically, locking the user or entity before it can delete, download, or share these files, preventing a breach.
3. Stopping Ransomware Attacks
Ransomware prevention is another leading UEBA use case in healthcare. Ransomware attacks against healthcare organizations have more than doubled between 2016 and 2021. Stopping these incidents early is critical to minimizing damage and protecting patients’ privacy. UEBA provides that speed.
Before ransomware can steal or lock any files, it must access them all. However, UEBA will notice an unknown program suddenly trying to access a large amount of data, restricting access and isolating the file, account, or device from which the ransomware spreads before it can encrypt anything.
4. Preventing Insider Threats
UEBA is also valuable for addressing insider threats, which are particularly prevalent in healthcare. Insider error accounts for more than twice as many breached medical records as malicious activity. UEBA can detect anomalies and prevent these mistakes. If a staff member tries to access something they don’t usually need, UEBA would flag it as suspicious, bringing the issue to their attention. If it were malicious, UEBA would stop them from abusing their privileges.
UEBA can also identify and halt actions like sharing credentials or attempts to send files to unauthorized users, preventing employees from falling for phishing attempts, which account for most insider threats.
5. Securing IoT Endpoints
As IoT adoption in healthcare grows, IoT security becomes an increasingly advantageous UEBA use case. UEBA includes endpoints, enabling it to address IoT concerns, unlike UBA systems that only consider people. Just as UEBA spots irregular behavior in user accounts, it can detect unusual connections or access attempts from IoT devices, stopping hackers from using a smart device with low built-in security as a gateway to more sensitive systems and data.
Stopping this lateral movement is crucial because IoT devices typically have weak security, and hospitals use many of them, with more than half featuring critical known vulnerabilities.
Behavioral Analytics Are a Must for Healthcare
UEBA use cases highlight the potential of this technology for medical organizations. As EHR adoption and cybercrime rise, leveraging these applications will become increasingly important. The healthcare industry must take cybercrime seriously, with user and entity behavioral analytics systems being some of the most effective tools for achieving that goal.
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