Powered by GenAI and increasingly accessible fraud tools, fraud threats are evolving faster than ever. Traditional fraud detection solutions alone are struggling to keep up with evolving fraud rings, fraud bots, and attack strategies, pushing businesses to explore smarter, more adaptive defenses. That’s why many organizations are turning to User and Entity Behavior Analytics (UEBA) as protection against growing threats, especially internal ones. But what exactly is UEBA, and how does it differ from other solutions, like behavioral analytics?
What is UEBA?
User and Entity Behavior Analytics is a cybersecurity and fraud prevention approach that uses behavior monitoring, network data, and machine learning to analyze users and entities (like devices, applications, and servers) within a network.
By establishing a baseline of normal behavior and system usage, UEBA can detect anomalies that may indicate malicious activity (for example: a user who rarely uses large files downloading 5 GB of data in a short period of time, or one attempting several failed authentications). In short, UEBA monitors how users and systems typically behave and raises a red flag when something unusual happens.
UEBA vs. behavioral analytics
Behavioral analytics and UEBA are closely connected, sharing many of the same signals and goals. But, while the two terms are similar and often used interchangeably, they serve distinct purposes for fraud prevention.
Behavioral analytics assesses risk based on how users interact with a website or mobile app session in real time. It evaluates data like mouse movements, keystrokes, swipes, and device and network intelligence to detect third-party fraud. These signals are typically used at the front end of digital interactions — during onboarding, login, or checkout flows — to prevent account opening fraud, account takeovers, fraudulent transactions, and more. Because it adds no additional user friction, behavioral analytics in fraud detection is a valuable first line of defense against fraud rings and bot attacks for financial institutions, merchants, fintechs, and other businesses that serve large volumes of external users.
UEBA functions similarly, but operates at a deeper level and often serves a narrower population. UEBA starts with many of the same signals as behavioral analytics, but extends to include application usage, system access, server activity, and interactions between users and non-human entities like devices, service accounts, and cloud resources. UEBA is typically used to detect internal threats, such as insider attacks, compromised accounts, or lateral movement within a network. It builds long-term baselines and identifies anomalies that may indicate a security risk.
Use cases for UEBA
By analyzing the behavior of users and systems, UEBA helps organizations flag security threats within their networks. Below are some of the most impactful use cases where UEBA adds protection for businesses:
- Insider threat detection: Detects employees or contractors misusing access to steal data or sabotage systems.
- Example: An employee accessing sensitive files they’ve never touched before.
- Compromised account detection: Identifies accounts being accessed by someone other than their authorized owner.
- Example: A user logs in from a foreign country and downloads large volumes of data.
- Lateral movement detection: Tracks how attackers move within a network after gaining initial access.
- Example: A user account starts accessing multiple servers it has never interacted with before.
A behavior-based approach to fraud prevention
As fraud threats continue to evolve, behavior-based approaches like User and Entity Behavior Analytics are crucial to stopping sophisticated attacks. Behavioral analytics — the core of UEBA — can be the first step towards a more modern fraud prevention strategy, capable of stopping advanced threats without compromising the customer experience.
Learn more about our behavioral analytics for fraud detection.
