Top UEBA Use Cases for Healthcare

Top UEBA Use Cases for Healthcare

User and Entity Behavioral Analytics: Enhancing Cybersecurity in Healthcare

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Security is a paramount concern in all industries, but perhaps none more so than healthcare. With hospitals storing vast amounts of highly sensitive patient data, they have become prime targets for cybercrime. As a result, healthcare organizations must employ extensive defense mechanisms. One tool that has proven invaluable in this endeavor is User and Entity Behavioral Analytics (UEBA).

Understanding User and Entity Behavioral Analytics

User and Entity Behavioral Analytics leverages machine learning to detect threats, such as breached accounts or ransomware, that may slip through the cracks of traditional security measures. Unlike multi-factor authentication, which aims to prevent attacks, UEBA focuses on identifying and stopping threats before they can cause significant damage.

UEBA examines the behavior of various users and entities, such as routers or Internet of Things (IoT) devices, within a network. By establishing baselines for normal behavior, machine learning algorithms can detect suspicious activity. For example, an account attempting to access a database it rarely interacts with or downloading data at an unusual time might be flagged as a potential breach. This proactive approach draws parallels to how a bank may freeze your credit card if it detects unusual purchases.

The Benefits of UEBA

UEBA offers several benefits across various applications. Let’s explore some of the most significant advantages it brings to the table.


Behavioral analytics systems powered by machine learning algorithms excel in accuracy. They can identify trends and patterns in data that human analysts might miss, making UEBA tools more effective at determining what is and isn’t suspicious. When properly deployed, UEBA can achieve false positive rates as low as 3%, preventing security teams from wasting time and resources.

UEBA’s adaptability plays a crucial role in its accuracy. Machine learning algorithms continuously gather new data and adjust their decision-making as behavioral trends shift. This adaptability allows UEBA to account for nuances such as users gradually adopting new habits or activities being normal in certain contexts but not in others.


UEBA offers significant time savings due to its fast detection capabilities. Machine learning tools can identify and classify anomalies almost instantly, whereas it might take a human analyst a few minutes to do the same. These time savings can make a considerable difference when dealing with cyber threats.

By detecting suspicious behavior early, UEBA tools can often prevent an account or compromised device from causing any real damage. IBM found that reducing data breach response timelines saves organizations an average of $1.12 million.


Compared to similar security tools, UEBA stands out for its versatility. While some organizations employ User Behavior Analytics (UBA), which focuses solely on user activity, UEBA goes a step further by incorporating entities. This expanded perspective allows UEBA to detect a wider range of incidents, including IoT attacks and hardware breaches.

Moreover, machine learning tools like UEBA offer greater flexibility compared to rule-based anomaly detection systems. AI models can adapt to changing situations and account for situational differences, providing healthcare organizations with an invaluable asset as telehealth has grown exponentially since the onset of the COVID-19 pandemic.

UEBA Use Cases in Healthcare

UEBA’s benefits become even more evident when considering its application in healthcare. Here are five of the most impactful use cases for user and entity behavior analytics in the medical industry:

1. Automating Risk Management

UEBA’s most beneficial use case in healthcare is automating risk management. IT monitoring is crucial in the field, but many organizations lack the time or staff to manage it manually. Furthermore, the healthcare industry faces a skills gap in the cybersecurity talent pool. On top of that, electronic health records (EHRs) have increased the workload of medical workers, with over 70% reporting longer hours.

UEBA alleviates this burden by autonomously handling network threat detection, eliminating the need for large security teams to monitor systems round the clock. By leveraging UEBA’s accuracy and efficiency, medical professionals can use electronic systems more effectively, reducing verification stops and false positives. These time savings translate into improved cybersecurity and enhanced patient care.

2. Detecting EHR Breaches

Detecting and responding to breaches in electronic health records is another critical use case for UEBA in healthcare. While EHRs streamline data management, they also introduce significant security risks. In 2022 alone, there were over 700 health record breaches involving 500 or more records, averaging nearly two breaches daily. UEBA proves indispensable in addressing this widespread and severe issue.

By analyzing user and entity behavior, UEBA can identify when an application or account is accessing an unusual number of records or interacting with them in abnormal ways. Prompt detection enables organizations to intervene before sensitive files can be deleted, downloaded, or shared, effectively preventing breaches.

3. Stopping Ransomware Attacks

Another prominent use case for UEBA in healthcare is preventing ransomware attacks. The rise of ransomware-as-a-service has made these attacks increasingly prevalent, with the medical industry becoming a prime target. Between 2016 and 2021, ransomware attacks against healthcare organizations more than doubled.

UEBA’s speed plays a vital role in minimizing the damage and protecting patient privacy. By detecting when an unknown program is attempting to access a large amount of data, UEBA can restrict access and isolate the compromised file, account, or device, effectively preventing encryption and the subsequent loss of sensitive information.

4. Preventing Insider Threats

UEBA is a valuable tool for addressing insider threats, which are particularly prevalent in the healthcare industry. Insider error accounts for more breached medical records than malicious activity—more than double, in fact. UEBA’s ability to detect anomalies extends to both internal and external threats, making it an effective solution for preventing mistakes made by well-intentioned employees.

If a doctor, nurse, or staff member attempts to access unauthorized areas or performs actions inconsistent with their usual behavior, UEBA will flag it as suspicious. This intervention brings attention to the employee, allowing them to rectify accidental actions or preventing malicious insiders from abusing their privileges. UEBA can also detect and halt activities like sharing credentials or attempts to send files to unauthorized users, effectively preventing employees from falling victim to phishing attempts.

5. Securing IoT Endpoints

With the increasing adoption of IoT in healthcare, securing IoT endpoints emerges as a highly advantageous use case for UEBA. As traditional user behavior analytics (UBA) systems solely focus on people’s behavior, they fail to address IoT devices. In contrast, UEBA’s broader perspective includes entities, allowing it to handle IoT-related concerns effectively.

Just as UEBA detects irregular behavior in user accounts, it can identify unusual connections or access attempts from IoT devices. By doing so, UEBA can prevent hackers from using vulnerable or poorly secured smart devices as gateways to more sensitive systems and data. Given that over half of medical IoT devices have critical known vulnerabilities, bolstering IoT security becomes paramount for the industry.

Embracing Behavioral Analytics in Healthcare

The aforementioned use cases merely scratch the surface of what UEBA technology can achieve for healthcare organizations. As the adoption of EHRs increases and cybercrime continues to escalate, leveraging UEBA will become increasingly crucial.

It is imperative for the healthcare industry to take cyber threats seriously. User and entity behavioral analytics systems provide some of the most effective tools for safeguarding patient data and mitigating cybersecurity risks.

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