User and Entity Behavior Analytics: The Complete Guide

Imagine a world where cyber threats are caught before they strike. A world where your organization’s data is protected by an invisible watchdog, always alert and never tiring.

User and entity behavior analytics for enterprise security can anticipate and neutralize threats before they can impact your organization – through User and Entity Behavior Analytics (UEBA).

Gone are the days of reactive defense. User and entity behavior analytics brings a proactive approach, using advanced analytics to spot the subtle signs of a brewing attack.

But what exactly is UEBA? How does it work? And why should your organization care?

User and Entity Behavior Analytics (UEBA): Definition and Core Concepts

User and Entity Behavior Analytics (UEBA) is a cybersecurity process that takes note of the normal conduct of users and entities within an organization’s network and then identifies anomalies in these patterns.

But what exactly does this mean in practice?

UEBA incorporates behavioral biometrics, recognizing that users and entities within a network exhibit unique, consistent patterns in their interactions with systems, much like a digital fingerprint.

When these behaviors deviate significantly from the norm, it could indicate a potential security threat.

For instance, if an employee who typically accesses the company’s database during regular business hours suddenly logs in at 3 AM from a foreign IP address, UEBA would flag this as suspicious activity.

This approach differs significantly from traditional rule-based security systems. While rule-based systems rely on predefined scenarios to detect threats, UEBA uses machine learning and artificial intelligence to adapt and evolve its understanding of normal behavior over time.

This dynamic approach allows UEBA to detect subtle, complex threats that might slip through static rule-based defenses.

Context is another crucial aspect of UEBA. It’s not enough to simply identify anomalous behavior; UEBA must also understand the context in which this behavior occurs.

For example, a sudden increase in data transfers might be suspicious for one department but normal for another during a specific project phase. UEBA systems take into account various contextual factors such as time, location, role, and typical workflow to determine whether an anomaly represents a genuine threat or a benign deviation.

By combining these elements – behavioral analysis, machine learning, and contextual understanding – UEBA provides a sophisticated, adaptive approach to cybersecurity that can keep pace with evolving threats.

Key Components of UEBA

Data Collection and Integration

The foundation of any effective UEBA system is comprehensive data collection and integration.

UEBA relies on a wide variety of data sources to build a complete picture of user and entity behavior within an organization’s network. These sources typically include log data from various systems and applications, network traffic data, and user activity logs.

Log data is particularly valuable as it provides detailed information about system events, user logins, file access, and other critical activities. Network traffic data offers insights into communication patterns, data transfers, and potential malicious connections.

User activity logs capture information about how individuals interact with systems and data, including actions like file modifications, uploads, and downloads.

The quality and completeness of this data are also paramount. Incomplete or inaccurate data can lead to false positives or, worse, missed threats. Therefore, organizations must ensure that their data collection processes are robust and cover all critical systems and activities.

One common challenge in data collection is dealing with data silos. Different departments or systems within an organization may store data separately, making it difficult to get a comprehensive view. To overcome this, organizations should focus on breaking down these silos and implementing data standardization practices.

This might involve using common data formats, establishing clear data governance policies, and leveraging data integration tools.

If you’re looking for clear, centralized policies for your org, you probably also want visibility and control over your data so your entire team is on the same page. Explore Qohash’s Qostodian to monitor your high-risk, unstructured data.

Behavioral Baselining

Behavioral baselining is a critical component of UEBA that involves establishing a “normal” pattern of behavior for users and entities within a network. This baseline serves as a reference point against which future activities can be compared to identify anomalies.

There are several methods for establishing baselines. One common approach is peer group analysis, where users or entities with similar roles or characteristics are grouped together, and their collective behavior forms the baseline.

Another method is historical pattern analysis, which looks at an individual user or entity’s past behavior to predict future actions.

It’s important to note that baselines aren’t static; they evolve over time as user behavior and organizational processes change. For example, a company might implement a new software system, leading to changes in how employees interact with data. UEBA systems must be able to adapt their baselines accordingly to avoid flagging these legitimate changes as anomalies.

Anomaly Detection

After establishing behavioral baselines, UEBA systems employ sophisticated anomaly detection techniques to identify activities that deviate substantially from expected patterns, potentially signaling security threats. These techniques range from simple statistical methods to complex machine-learning algorithms.

One common approach is the use of statistical outlier detection, which identifies data points that fall outside a certain range of standard deviations from the mean.

Cutting-edge machine learning in cybersecurity techniques, such as clustering algorithms for behavior grouping and neural networks for detecting intricate, non-linear anomalous patterns, elevate UEBA’s threat detection capabilities.

Context is crucial in anomaly detection. Not all deviations from the norm are necessarily threats. For example, an employee working late to finish a project might trigger an anomaly alert for after-hours system access. UEBA systems must consider contextual factors like time of year (e.g., end of financial quarter), role-based expectations, and ongoing projects to differentiate between benign anomalies and genuine threats.

UEBA systems typically handle different types of anomalies.

Point anomalies are individual instances of unusual behavior, like a single unauthorized access attempt.

Contextual anomalies are behaviors that are unusual in a specific context but might be normal in others. Collective anomalies involve patterns of behavior that are anomalous when viewed as a group, even if individual actions seem normal.

One of the main challenges in anomaly detection is striking the right balance between sensitivity and specificity. If the system is too sensitive, it will generate many false positives, overwhelming security teams. If it’s not sensitive enough, it might miss genuine threats. Continuous tuning and machine learning algorithms that improve over time can help address this challenge.

Risk Scoring

Risk scoring is the process of assigning a numerical value to the level of risk associated with a particular user, entity, or behavior. This score helps security teams prioritize their responses to potential threats.

There are various methodologies for calculating risk scores. Some systems use a simple additive model, where different risk factors are assigned weights and summed up. Others employ more complex probabilistic models that consider the likelihood and potential impact of a threat.

Machine learning algorithms can also be used to dynamically adjust risk scores based on evolving patterns and newly discovered threats.

Risk scores play a crucial role in enhancing threat detection capabilities. It allows security teams to prioritize alerts and investigations based on the potential severity and likelihood of identified risks.

High-risk activities or entities are flagged for immediate attention, while lower-risk anomalies might be monitored or investigated as resources allow. This prioritization is crucial for security teams dealing with a constant flood of alerts and potential threats.

UEBA Use Cases in Data Security Posture Management

Insider Threat Detection

Insider threats, whether malicious or unintentional, pose a significant risk to organizations. UEBA excels in insider threat detection, leveraging its advanced analytics to identify minute behavioral shifts that may signal malicious intent or compromised user accounts within the organization.

UEBA helps identify insider threats by monitoring for indicators such as unusual access patterns, large data transfers, access to sensitive files outside of normal job duties, or attempts to elevate privileges.

For example, if an employee suddenly starts accessing a large number of files they’ve never accessed before, especially outside of their department, this could trigger an alert.

Privileged Account Abuse Prevention

Privileged accounts, which have elevated access rights within a system, are prime targets for attackers. If compromised, these accounts can cause significant damage. UEBA plays a crucial role in preventing privileged account abuse by closely monitoring the behavior of these high-risk accounts.

UEBA systems monitor privileged accounts for any signs of abnormal behavior, such as accessing systems or data outside their usual scope, performing actions at unusual times, or making configuration changes that don’t align with their typical activities.

For instance, if a privileged account suddenly starts accessing payroll data when it has never done so before, UEBA would flag this as suspicious.

It’s important to note that UEBA should be seen as a complement to, not a replacement for, existing PAM solutions. While PAM controls access, UEBA provides the behavioral context to identify when that access might be misused. Together, they create a robust defense against privileged account abuse.

How Qohash Incorporates UEBA in Its Solutions

We’re leaders in data discovery and classification, here to help you incorporate data security posture management tools and practices into your organization. Our tools help with data classification, continuous monitoring, and visibility so you can have a more comprehensive approach to data protection.

Request a demo today to see the power of comprehensive data security.