Traditional security measures to deal with cybersecurity threats are no longer enough to protect a company’s sensitive data and assets. Therefore, companies need a solution that can detect and respond to potential threats in real time, and that’s where user and entity behaviour analytics (UEBA) comes in.
In this article, we’ll explore UEBA in more detail, discuss its benefits, challenges, and best practices, compare it to traditional security measures, and more.
What’s User and Entity Behaviour Analytics (UEBA)?
UEBA is a security solution that employs machine learning algorithms to identify suspicious activity from users and entity behaviour that could indicate potential threats. UEBA solutions usually analyse data from different sources, including network traffic, logs, and user activity, to identify patterns of behaviour indicating an insider threat or breach.
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Furthermore, UEBA solutions can reduce false positives and negatives, provide early detection and response to potential security threats, and improve cyber threat hunting and compliance measures. UEBA is becoming increasingly crucial as companies confront more advanced cybersecurity threats that can evade standard security measures.
What Are the Pillars of User and Entity Behaviour Analytics?
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1. Use Cases
UEBA tools provide data on the behaviour of users and other entities in the business network, performing user behaviour analysis and identifying and alerting to peculiarities. UEBA solutions, on the other hand, must identify and handle multiple diverse use cases and detect a wide variety of threats, unlike specialist team monitoring systems (such as intrusion detection systems) or tools designed to detect breaches.
2. Data Sources
UEBA systems can import data from different specified sources, including a general data repository such as a data warehouse or data lake, security information and event management (SIEM) system, or log management integration. Data collection shouldn’t require deploying specialised agents in the IT environment.
UEBA security tools detect peculiarities using a variety of highly sophisticated and advanced analytics, including machine learning, rules-based monitoring, statistical analysis, algorithms, behavioural analysis, and threat signature detection.
What Are the Benefits of the UEBA System?
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1. Early Detection and Response to Threats
UEBA systems use machine learning algorithms to assess real-time entity and user behaviour. Because of this, companies can identify internal and external threats ahead of time and implement preventive measures before any damage is done.
2. Reduced False Positives and Negatives
UEBA systems usually analyse large amounts of data to identify behaviour patterns. As a result, security teams can spend more time investigating actual threats rather than false positives.
3. Enhanced Compliance
UEBA systems can identify policy violations and implement security measures, helping companies comply with industry rules and regulations.
4. Better Threat Hunting Abilities
UEBA systems can analyse vast volumes of data and identify threats that could get past traditional security measures. As a result, this helps security analysts carry out more effective threat hunting and investigations.
5. Improved Insider Threat Detection
UEBA systems can identify out-of-the-ordinary behaviour by employees, contractors, and other insiders, allowing companies to detect and respond to insider threats.
Challenges of User and Entity Behaviour Analytics
Although there are many benefits to using UEBA solutions, there are also some challenges that companies may encounter when using them. To address these difficulties, the company should carefully plan, collaborate across teams, and commit to ongoing analysis and refinement of the UEBA solution. Here are some of UEBA’s most common challenges.
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1. Data Integration
As mentioned earlier, UEBA solutions usually require integrating data from different sources, including network traffic data, security logs, and user activity logs. This can be challenging, particularly when the data is stored in different systems or formats.
2. Data Quality
The success of UEBA depends on the quality of the data used to train the machine learning (ML) models. The models tend to produce false positives or miss actual threats if the data is insufficient, biased, or inaccurate.
3. False Positives
UEBA tends to produce many false positives during the training of models or after any significant environmental changes. This can result in alert fatigue and reduce the system’s effectiveness.
4. Privacy Concerns
The UEBA system requires analysing user and entity behaviour, which might give rise to privacy issues. To address these concerns, companies must be transparent about the data they collect and how it will be used.
UEBA is a complex system requiring cybersecurity expertise, machine learning, and data science. It might be challenging for companies to hire qualified employees or implement enough infrastructure to support UEBA.
User and Entity Behaviour Analytics (UEBA) Best Practices
Following these best practices will help you get the most out of UEBA systems.
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1. Define Clear Objectives
Before adopting UEBA, it is crucial to establish concrete goals. What are the specific security threats your organisation wants to address? What results do you anticipate? Establishing concrete goals will help the organisation select the right UEBA solution and ensure it’s configured properly to meet its needs.
2. Choose the Right Data Sources
UEBA systems rely on data from various sources, including network traffic, logs, and user activity; therefore, choosing the right ones is important. Companies should determine which data sources are most handy for their specific needs and ensure they are compatible with the UEBA solution.
3. Choose the Right UEBA Solution
Keep in mind that not all UEBA options are the same. Therefore, it is crucial for organisations to thoroughly research the UEBA options available to them before settling on one. The key factors to consider are the solution’s scalability, ability to identify many threats and ease of use.
4. Ensure Proper Configuration
For UEBA solutions to effectively identify and respond to threats, they must be set up properly. This entails configuring appropriate alert thresholds and ensuring the system is set up to analyse the right data sources.
5. Provide Enough Training
UEBA solutions can be complicated, so ensure your security team has the training to use them effectively. This includes training on responding to potential security threats, interpreting alerts, and customising the UEBA solution to meet the company’s needs.
6. Monitor and Review Results
UEBA systems should be monitored and assessed often to ensure they deliver reliable threat detection and response capabilities. Besides, regular reviews can help companies identify ways to enhance the UEBA solution and ensure it’s offering the expected outcomes.
What’s the Difference Between UEBA and UBA?
User and Entity Behaviour Analytics (UEBA) and User Behaviour Analytics (UBA) are two related but different systems used in cybersecurity to identify and prevent insider threats and other security breaches. The key difference between UEBA and UBA is the scope of their analysis.
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UBA usually focuses mainly on evaluating user behaviour patterns to detect peculiarities that could indicate a security breach. This includes unusual data access patterns, login times or places, behaviour, and file transfers.
Conversely, UEBA broadens the focus of analysis beyond user behaviour to include the behaviour of other entities like applications, devices, and even whole networks. UEBA can detect peculiarities in the behaviour of these entities that might represent a security threat.
What’s the Difference Between UEBA and SIEM?
User and entity behaviour analytics (UEBA) systems and security information and event management (SIEM) are two distinct but complementary techniques to security analytics that help companies identify and respond to potential security threats.
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SIEM provides real-time analysis of security alerts created by the network hardware and applications. It usually collects, correlates, and analyses log data from different sources to offer a birds-eye perspective of security events across the company’s IT network. Besides, SIEM systems offer automated alerts, reports, and workflows to security teams, which help them investigate and respond to potential security breaches.
Conversely, UEBA uses machine learning algorithms to analyse user and entity behaviour throughout a company’s IT infrastructure. As mentioned earlier, UEBA systems often collect and analyse data from different sources, including user behaviour, network traffic, and logs, to create a baseline of normal behaviour. They then use machine learning algorithms to identify unusual behaviour that could point to a security breach.
The key difference between UEBA and SIEM is that the former is designed to identify and detect insider and advanced persistent threats that standard security systems may miss. In contrast, the latter is designed to identify and respond to known security events.
Frequently Asked Questions on UEBA System
1. Why is UEBA so important?
UEBA usually enriches security data using reliable context data. The context data enhances event detection accuracy, reducing false positives and allowing threat hunting and context-based searching. UEBA’s analytics engine utilises machine learning, threat modelling, and behaviour analytics to identify top threats.
2. Can the UEBA system detect compromised accounts?
UEBA empowers the security team to expose compromised accounts, insider threats, and privilege misuse in real time.
3. How does UEBA system work with artificial intelligence?
UEBA utilises statistical analyses and machine learning approaches to detect unusual network behaviour. After UEBA creates a baseline for each entity’s expected behaviours in the network, it examines the data and analyses all activities against these baselines.
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