User behavior analytics (UBA or entity behavior analytics) technology focuses on what the user is doing (unlike firewalls & antiviruses) to search for patterns of usage that indicate anomalous behavior.
UBA keeps a close watch on activities performed- from apps used to network activity and most critical files accessed, everything gets tracked regardless of whether the activities are coming from an insider, hacker, or even malware.
Although UBA won’t prevent hackers from getting inside your system, it can surely minimize the damage and spot their activities. Hackers have become very good at appearing like ordinary users, and that’s where UBA comes in for rescue by focusing more on user activities and less on system events.
UBA understands user patterns, including login times, geographical locations, session duration, network traffic, file downloads, and authentication logs and zeroes down on the hackers if their behavior differs from legitimate users.
What is User Behavior Analytics
UBA is a cybersecurity technology that tracks a system’s users to detect targeted attacks, insider threats, and financial fraud by using monitoring tools before a data breach, enabling threat detection and continuous risk assessment without complicating the end-user experience.
While SIEM (Security Information and Event Management) analyzes events that occur behind firewalls, UBA analyzes user behavior through machine learning to detect anomalies that recognize potential threats.
Primarily, user behavior analytics relies on automated analysis of big data to identify and stop potential cyberattacks in real-time. Big data platforms are increasing UBA functionality by analyzing petabytes worth of data to detect advanced persistent threats.
Perimeter-facing enterprise security technologies like encryption or Firewalls cannot stop malicious insiders who have already gained access to internal data through phishing, credential theft, or malware. On top of it, the widespread adoption of cloud, SAAS, and mobile applications have made risk management difficult.
The global average cost of a data breach is nearly $4 million, indicating 34% of a data breach is from internal factors, making UBA very important to recognize insider threats before they can perform damaging activities or theft.
Many of the applications are not officially approved by the IT department, making it even more challenging for the IT teams to identify potential threats across hybrid architectures.
UBA addresses this challenge by continuously monitoring every user activity, then anomaly detection to identify and flag anomalous behavior before a breach.
UBA is essential for organizations to eliminate security blind spots, identify intrusions, protect data, and respond to security events proactively.
How Does it Work?
UBA works by establishing benchmarks for normal human behavior and simultaneously alerting the IT team when a user deviates from predefined rules.
For example- if a user attempts to sign in at unusual hours at 3 AM, the UBA would flag that behavior and halt access immediately or alert IT admins to prevent a serious breach.
Sophisticated UBA solutions use machine learning for rulemaking capability, where it is capable of detecting unusual behavior through a dynamic rule that creates specific risk profiles for each user.
These risk profiles are created by observing how each user works, how they access devices, share files for their projects, what apps they use, preferred networks and devices. When a user exhibits abnormal behavior, these sophisticated UBA solutions autonomously take action in real-time to block the user’s device before data is compromised.
What to Look for in a User Behavior Analytics Product?
It’s imperative to choose a security analytics tool that best fits your organization’s security operations, users, and infrastructure, considering every UBA software is different from another.
Cyber thieves go after data, including files and emails, so user and entity behavior analytics that support canned rules only, pure perimeter-based analysis won’t be able to keep up with clever hackers. UBA with access to granular files and email activity has a better chance of providing complete data analysis.
Presenting below a list of key factors to consider for a UBA solution that can take on the new generation of hackers-
Dynamic Rule Making
A good UBA software must create dynamic rules for normal user behavior instead of depending upon predefined benchmarks. By implementing machine learning solutions, it will become difficult for hackers to mimic their behavior. ( Because every user is unique with personal activity patterns)
Ability to Process Huge Amount of User File & Email Activity
To prevent spreading enormous, sensitive data like a proverbial needle in the haystack, a good UBA engine should search and analyze the key metadata with the activities of multiple users across enormous volumes of data.
A good user behavior analytics solution is known for its hacker-detection algorithm’s ability to track file activities across a large population in real-time. UBA software needs to respond quickly, considering a small time window for when they touch sensitive data.
Delivering a high-quality work experience is equally important, just like protecting your sensitive data. So, your UBA solution must provide sophisticated risk profiles and risk scores for every employee to prevent unnecessary disruptions that may hurt their productivity.
An over-aggressive software may lose access to essential apps & data by flagging suspicious behavior, even if the employees have slightly deviated from routine activity.
Integration Across all Infrastructures
Many organizations prefer behavior analytics tools that work perfectly inside a secure workspace containing all work data and applications to avoid an IT blind spot that hackers may take advantage of. A blind spot happens when your UBA solution fails to integrate with your existing technologies.
On average large companies use 129 applications, and smaller companies use 73 apps. Choose a software capable of learning from user behavior across all your cloud devices, applications, and other devices that employees are using.
Top User & Entity Behavior Analytics Tools 2022
User and entity behavior analytics(UEBA) tools are a vital computer network security measure aiming to detect compromised accounts, changes in permissions, insider threats, and the creation of super users with administrator privileges.
UEBA tools developed because of an increasing complexity of intrusion strategies and malicious activities, which were difficult to detect through preventive measures. These tools are well-equipped to detect brute force attacks and breaches of protected data through a sudden change of behavior activities.
Let us check few of the top UEBA tools 2022–
EmpMonitor is making organizations more productive, compliant, and secure with its AI-powered user behavior analytics, super-advanced data breach response & monitoring user activity from a centralized location.
This remote management tool is advanced, intelligent, and integrated to lower the risk, and you can access your account from any internet-enabled device from anywhere at any time.
This highly recommended, unified platform identifies and classifies your sensitive, regulated, or mission-critical information accurately( including cloud data and on-premises data).
Furthermore, it reduces the exploration of sensitive content by implementing risk-appropriate security controls and detecting abnormal activity before and responding before a threat turns into a breach.
Activtrak uses workforce analytics and employee monitoring software to implement UEBA security measures. It uses deep data analytics, uncovering how employees work to provide the workflow.
In addition, it is an effective remote workforce management tool, tracking and analyzing trends, activity analytics establishing a top-performing baseline.
Varonis is insider threat detection software providing data security solutions, including tools for data protection, response, compliance, and threat detection.
Its threat detection protocol is built around user behavior analytic! Varonis use predictive threat models to analyze behaviors across multiple platforms providing the ability to detect CryptoLocker infections, compromised service accounts, employee behavior, and cloud platforms like- AD, Sharepoint, Exchange, Windows, Office 365, Dell EMC, HPE, Box, and Unix/ Linux.
IBM’s QRadar analyzes user activity to determine whether user credentials are compromised. It is an integrated component using behavioral models, rules, and machine learning to provide additional user context to network, vulnerability, log, and threat data to display and detect attacks in real-time.
QRadar’s security intelligence platform lets security analysts identify risky profiles and their activities with drill-down capabilities to log and flow data.
Azure Advanced Threat Protection (ATP) protects user identities & credentials particularly stored in the active directory. The software identifies and investigates suspicious activities of users and potential threats.
Additionally, it has features for user monitoring, entity behavior, and learning-based activities with clear incident information on a timeline for fast triage & remediation.
Microsoft is planning to change the name in the coming future, but ATP will continue providing solutions for protecting on-premises identities to prevent, detect, and investigate attacks.
Splunk is a UEBA tool that makes a distinction between user and entity behavior. By using machine learning, Splunk detects abnormalities missed by traditional safety tools. These analytic tools are used in financial services, the public sector, and the healthcare sector.
Splunk simplifies the tasks of security analysis by automating the stitching of multiple anomalies into a single threat. Deep investigative capabilities and behavior baselines help accelerate threat hunting.
Prisma Cloud is a Palo Alto Networks product, a comprehensive cloud security solution that offers security, workload protection, infrastructure entitlement management, posture management, and network security.
The system combines rule and behavior-based analytics & augments data from over 30 unique sources for threat intelligence. Also, it includes different modules that include network anomaly detection & UEBA to help detect and respond to threats.
Moreover, it quickly monitors, tracks user activities, and gets alerts from deviations from established normal behavior via an ML-powered UEBA engine.
Attackers are using artificial intelligence(AI) technology to develop complex ways to penetrate safer security networks, making legacy signature-based systems less effective in securing modern networks.
User behavior analytics security is to complement and enhance existing security solutions with advanced comprehensive coverage. UEBA security tools mitigate threats and prevent security breaches by recognizing activities that deviate from baseline for standard network activity.
UEBA security solutions detect threats, even if it is not clear what kind of attack it is, including insider threats, after perimeter-focused security solutions fail.
EmpMonitor’s user behavior analytics is AI-powered to help you detect any abnormal activity before it turns into a breach. Use it now-