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What is User Entity Behavior Analytics (UEBA)?

According to the 2017 Verizon on Data Breach Investigations Report:

  • 91% of companies have poor strategies for detecting insider threats.

  • 81% of data breaches include compromised or stolen credentials.

  • 69% of businesses report instances of attempted data theft via internal risks.

To prevent a data breach, your firm must promptly identify and react to suspicious behavior. User and entity behavior analytics solutions (UEBA) help you monitor for known cyber threats and behavioral changes in user data, therefore offering crucial insight into user-based threats that could otherwise go undiscovered.

Since user-based attacks are on the rise, UEBA security is crucial. In a study conducted by Crowd Research Partners in late 2017, 38% of firms were found to be using UEBA security solutions. This percentage is likely to be higher now, given the market for UEBA technology is expanding rapidly. Despite growth rates of 40%, Gartner anticipates that UEBA technology will be absorbed by other security solutions. According to a 2018 Gartner research titled Market Guide for User and Entity Behavior Analytics, security teams are moving away from prevention-only strategies in the field of cybersecurity. As security teams shift toward balancing cyber threat prevention with the newer detection and incident response approaches, they are adding technologies such as User and Entity Behavior Analytics (UEBA) to their conventional SIEMs and other legacy prevention systems to improve the effectiveness of their security systems.

In this article, we will explain what User and Entity Behavior Analytics is, how it works, why organizations need a UEBA solution, the benefits, and drawbacks of UEBA tools, use cases of UEBA, and its distinctions with similar technologies like UBA, NTA, SIEM, and XDR. We will also examine the top UEBA tools available in the market.

What Does UEBA Stand For?​

User and Entity Behavior Analytics (UEBA) is a cybersecurity system that uses machine learning, algorithms, and statistical analyses to monitor and identify anomalous traffic patterns, illegal data access and movement, and suspicious or malicious activities on networking and endpoint devices including routers, servers, desktops, etc.

The User and Entity Behavior Analytics system is a component of a multi-layered, integrated IT and information security strategy designed to prevent intrusions and analyze risks. UEBA aims to identify any abnormal or suspicious activity - occasions in which there are deviations from routine patterns or use. For instance, if a certain user on the network seldom uploads tiny data to the Internet but suddenly begins uploading 2 GB of files, the UEBA system would consider this an abnormality and either notify an IT administrator or, if automation is in place, disconnect the person from the network.

UEBA goes beyond monitoring human behavior; it also monitors equipment. One day, a server at a single branch office may get thousands more requests than normal, suggesting the beginning of a possible distributed denial-of-service (DDoS) attack. There is a possibility that IT managers might miss this sort of behavior, but the UEBA would identify it and take appropriate action.

User and Entity Behavior Analytics is a very strong instrument for detecting compromises early, mitigating risk, and preventing data exfiltration.

How Does UEBA Work?​

A UEBA solution must be deployed on every device used by or linked to every employee in the business for it to be effective. This covers both company-owned and employee-owned devices since even devices used intermittently are targets of a cyber attack. Some companies require workers to install the UEBA solution on their home routers, which might act as attack vectors. Connecting to the business network using a home router increases the likelihood of a cyber attack.

The UEBA solution becomes "silent" when it begins to gather device and network use statistics. In the learning mode, the algorithms of the UEBA solution will identify and further define what is deemed normal or even optimum. IT administrators choose the duration of the learning mode before the system enters testing mode.

A UEBA solution consists mostly of three elements:

  • Analytics: Analytics analyzes and organizes data on what it deems to be typical user and entity activity. The system creates profiles for each user based on their typical program use, communication and download behavior, and network connection. Then, statistical models are developed and used to identify anomalous activity.

  • Integration: Integration with existing security products and systems is essential for growing and changing companies. They probably have a security stack in place, which contains antiquated technology that cannot keep up with the constantly evolving threat environment of today. The beauty of UEBA is that it is not intended to replace current enterprise-wide security technologies. UEBA systems can compare data acquired from many sources, including logs, packet capture data, and other datasets, and combine them to make the system more resilient with correct integration.

  • Automated Response: Automated response is the process of conveying the UEBA system's results and formulating a suitable response. This varies across groups. Some UEBA systems merely provide an alert to the employee or IT administrator, recommending additional inquiry. Other UEBA technologies are configured to take quick action, such as disabling the employee's network access in the event of a suspected cyber attack.

Why Do You Need UEBA?​

Cyber attacks are growing increasingly sophisticated and difficult to detect. Traditional security solutions such as web gateways, firewalls, intrusion detection, and prevention systems (IDS/IPS), and virtual private networks (VPNs) can no longer defend a company from intrusion. Sophisticated cyber attackers will find a method to penetrate a system, and it is vital to notice even the tiniest anomalies.

Additionally, social engineering and phishing are on the increase. These techniques do not target the hardware of a business, but rather its personnel, persuading them to click on links, download software, and transmit passwords. Infecting a single machine is only the beginning of a potentially massive attack. UEBA aims to identify even the most minute of anomalous activities and prevent a simple phishing campaign from growing into a big data breach.

Furthermore, it is no longer practicable to attempt to construct correlation rules for thousands of distinct conceivable cases. This is particularly true for insider threats. When establishing rules such as "Send an alert whenever a user sends an email attachment greater than 4MB," you must examine each unique user and then create exceptions. As an example, graphic designers in the marketing department may often transmit big PDF files. UEBA replaces standard Boolean alarms with probabilistic models or risk factors derived from sophisticated analytics, rather than requiring your security specialists to manually whitelist every instance of this kind.

Consequently, UEBA delivers improved detection of insider threats compared to typical SIEM correlation rules. In addition, UEBA may be coupled with your cloud services, computers, mobile devices, and IoT assets to detect aberrant user activity and suspected lateral movement outside of your organization/network. User behavior analytics give significant time savings since teams no longer need to go through logs in disparate places to piece together an event narrative. A smart UEBA solution ingests data from all log sources, including Windows Active Directory, VPN, database, badge, file, proxy, and endpoints, and constructs a contextual narrative for your security teams to assess.

What Does UEBA Defend Against?​

UEBA solutions protect organizations against the following cyber threats:

  • Brute Force Attacks: Repeated efforts to access user accounts and networks constitute brute force attacks. A UEBA readily recognizes this as abnormal activity. In this circumstance, a UEBA notifies or automatically boots the attacker.

  • Insider Threats: Security software struggles to identify insider threats. A SIEM may readily identify a network intrusion, however, it may not detect unauthorized network activity. A properly built UEBA should know how users generally act and warn if they change.

  • Privilege Escalation: Privilege escalation gives a person access to more of a network. This helps hackers. A UEBA is configured to warn administrators when a user's rights are upgraded.

  • Compromised Accounts: An atypical user isn't usually an internal threat. An attacker may have stolen the user's account. Phishing often targets business personnel, leading to compromised user accounts. UEBAs identify compromised accounts when an attacker does anything unusual.

  • Data Breach: UEBAs monitor who accesses private data. It prevents data breaches by alerting users when they access non-work-related data.

What are the Advantages of UEBA?​

UEBA has a significant influence on an organization's security posture. Let's examine the advantages of UEBA and why businesses should consider implementing it:

  • Lowers Risk: The primary advantage of the UEBA system is that it lowers cyber risks. Prevention methods consisting of isolated security products are insufficient. As the number of devices and locations has multiplied, it has become more challenging for companies to counter a rising number of threats. As workplaces close, staff work remotely utilizing various devices linked to routers with access to the internet.

    Not only can the UEBA solution be downloaded into the home devices of workers, but it is also compatible with IoT and rugged devices located in warehouses, retail stores, and hospitals. Any device linked to a business network is susceptible to cyber-attacks. It is impracticable for an IT staff, regardless of size, to physically monitor every device in operation; UEBA eliminates a significant portion of this effort.

    It is essential to highlight that UEBA is utilized for both threat detection and compliance. Companies in regulated areas, such as financial services and healthcare, must adhere to certain security requirements. While common network monitoring technologies may assess whether the software has been updated with the latest security updates, UEBA goes many steps further.

    Detecting behavior abnormalities enables the IT team to discover, for instance, that a router was not configured with the industry-recommended highest level of security. This enables the team to resolve the matter promptly, sparing the firm from having to pay penalties or engage in legal action as a result of a violation.

  • Reduces the number of IT Analysts: As is the case with every workplace application that utilizes machine learning and artificial intelligence, software substitutes the time and effort of personnel who would otherwise do the task. The development of UEBA solutions will not result in a drastic decrease in headcount, even though this idea may thrill a great number of enterprises, while IT experts may shudder. This is due to two factors:

    In addition to interacting frequently with workers, larger companies with sophisticated security needs, such as international firms and governments, understand the need for extra IT personnel and security analysts to set up, operate, and administer the system. In addition, extra security analysts will need to be dispatched to the employee or hardware location if the business chooses against using automated response capabilities and instead prefers to study the anomalous activity before taking action.

    If a business determines that it requires fewer IT Syslog analysts after the UEBA system is on autopilot, those staff members may be redeployed to higher-value, perhaps more mission-critical initiatives.

  • Covers a Broader Variety of Cyber Attacks: The key advantage of UEBA is that it enables businesses to identify a broader variety of cyber threats. Among the sorts of threats that UEBA identifies include brute-force attacks, DDoS, insider threats, and compromised accounts.

    The UEBA system monitors not just human activity on devices, but also the devices themselves, such as routers, servers, endpoints, and Internet of Things (IoT) devices. The scope and complexity of cyber attacks have increased, and hostile attackers find it more profitable to hack a device than to obtain credentials from human users.

    Employees are likely to utilize at least a laptop and a smartphone to do work-related activities as device use continues to rise and printers and fax machines become less common. As the number of attack vectors has multiplied tremendously, this has encouraged hostile actors to target mobile devices.

  • Reduces Expenses: Further to the preceding argument, if a business needs fewer analysts to do the function that the UEBA system is performing, there will be a decrease in IT expenditures. However, this does not imply that the whole security analyst workforce must be terminated once the system is operational. In every scenario, machine learning needs human interaction. In addition, halting a ransomware attack in its tracks may be seen as a kind of cost savings. The UEBA would have prevented the company from paying cyber attackers to recover a system or losing money due to hours or days of lost production as a result of a virus attack that left the server inaccessible.

  • Offers Minimal Maintenance: Although configuring a UEBA tool is difficult, it needs minimal maintenance after the first installation. Once the UEBA system is equipped with the required data, behavioral baselines, and trained algorithms, it runs independently with little IT and security staff management. Aside from the occasional fine-tuning or inclusion of a baseline, the tool's machine learning algorithms automatically adapt to a changing security environment.

  • Provides Automated Response: When UEBA systems identify suspicious activity, they normally inform security personnel, but they may also stop attacks automatically. If set accordingly, UEBA may trigger an automated threat response to confine a suspicious user or entity and thwart an attack before it can do harm. It allows security experts more time to analyze an occurrence.

Advantages of UEBA

Figure 1. Advantages of UEBA

What are the Disadvantages of UEBA?​

There are disadvantages associated with obtaining and deploying a UEBA system. One is the cost. It may just be unattainable for certain firms. While the sophistication of UEBA is advantageous for large corporations with complex, evolving security requirements, it can be disadvantageous for small and medium-sized businesses that can handle threat detection and management with a variety of other point solutions, such as web gateways, firewalls, and VPNs. Other drawbacks of UEBA are as follows:

  • UEBA requires numerous cross-functional approvals and system configuration. UEBA solutions are not off-the-shelf; substantial training and customization are required. Before recognizing risks in the actual world, the computer must first learn the behavioral baselines using user behavior data. This procedure is time-consuming, thus enterprises should consider this before implementing a UEBA solution. The UEBA is beneficial as a long-term approach but is ineffective for immediate changes.

  • UEBA cannot identify particular security attacks.

  • UEBAs rely on third-party logs to monitor, detect, and evaluate possible threats and give risk ratings. UEBAs cannot perform their duties if/when a third-party logger fails.

  • UEBA necessitates specialized knowledge and abilities since each company requires distinct user behavior datasets. Given the diversity in the roles of users and entities across companies, generic datasets are ineffective. The preparation of these datasets is difficult and requires ML training skills. To construct their datasets, organizations must either educate in-house specialists or contract with outside parties.

  • UEBA provides a limited picture of network behaviors and events since UEBA logs are only enabled on a subset of a company's network.

  • Many manufacturers say that UEBA may be installed in a few days, however, Gartner clients estimate that it typically takes 3-6 months for basic use cases and up to 18 months for complicated use cases.

  • Implementing UEBA involves substantial time and effort, and each component of the setup (configuration, training, and integration) has related expenses. Due to the complexity of UEBA technology, businesses must engage AI professionals, which increases the price of the UEBA instrument.

  • Its slow attack detection is less effective; UEBA is most successful when people or entities suddenly deviate from their typical behavior. For instance, it may quickly identify an intruder who compromises an accounting system or steals data as an isolated incident. Nevertheless, some attacks develop slowly, particularly if they include infiltrators or hostile insiders who plan and execute their operations over an extended time. Occasionally, these attacks include extremely tiny activities repeated every day for many months, such as the transmission of small quantities of sensitive data. These behaviors may be considered suspicious by UEBA tools.

How to Implement UEBA?​

According to Gartner's Market Guide for User and Entity Behavior Analytics, independent UEBA products are often installed on-premises or made available as a cloud service (with some requiring both).

In addition to appliances (virtual or physical) for monitoring network traffic and endpoint agents, many standalone UEBA providers need businesses to install or deploy software for the solution's main components. Some have unique data platform requirements, such as mandating that data be transmitted to an independent data lake controlled by the vendor.

Typically, alerts issued by standalone UEBA systems are shown on a UEBA-specific interface. These may identify known harmful activities, but more often they highlight suspect behavior requiring inquiry.

Gartner suggests beginning the implementation of a UEBA solution with a limited number of well-defined use cases and a modest amount of data.

In any event, the majority of organizations who adopt a UEBA solution discover that it takes at least three to six months to get the system up and running and calibrated(so that various log sources are given the appropriate weighting in the overall analysis in order to realize UEBA's advantages.

What are the Use Cases for UEBA?​

By concentrating on what people and systems do, UEBA technologies unearth important information for an expanding number of use cases. The most common use cases of UEBA are explained below:

  • Data Loss Prevention (DLP): UEBA prioritizes and integrates warnings from Data Loss Prevention (DLP) solutions used by several major enterprises to determine whether alerts indicate unusual activity. This decreases alert fatigue and expedites the identification of a true data breach by analysts.

  • Entity Analytics (IoT): IoT provides a significant security problem, as some firms manage tens of thousands of IoT devices deployed in the field with little insight over their activity and minimal security capabilities. UEBA monitors a limitless number of connected devices, creates behavioral baselines, and detects anomalous or malicious activity, such as connections from atypical sources, activity at atypical times, or device features that are not generally used.

  • Incident Prioritization: UEBA is capable of intelligently predicting whether instances are aberrant, suspicious, or possibly harmful. They go beyond correlation rules or attack patterns to detect undetected malicious behavior. It provides context for the organizational significance of assets; for instance, even a little divergence from normal behavior is important for a system containing crucial data.

  • Compromised Insider: UEBA systems can immediately identify malicious activity carried out by attackers who gain unauthorized access to a privileged account. They identify lateral movement, which consists of attackers moving to various computers or user accounts to penetrate IT systems further.

  • Malicious Insider: UEBA systems detect harmful insiders even though their activity seems benign to conventional security technologies. It does this by creating a baseline of each user's regular behavior and identifying when it deviates from this norm.

  • Fraudulent Transactions: Banks and other financial institutions, as well as service providers such as phone companies, have used these technologies for fraud detection for a long time. These systems were among the early implementations of the analytic methods used by UEBA instruments. In these circumstances, tools concentrate on unusual behavior, such as the following:

    • Unusual usage of ATM cards or online banking
    • Unanticipated patterns of charges on credit cards
    • Strange patterns in insurance claims
    • Long-distance toll fraud
    • Automated phone calls.

  • Cause and Effect Analysis: Occasionally, a single issue may affect numerous systems and levels of operation. To identify the links, threat analytics is required. For instance, sporadic transaction failures across many staff- and customer-facing apps and occasional issues with a database server and application containers operating on a particular Kubernetes cluster might be caused by a problem with the storage network underpinning them all.

    UEBA fulfills the operational requirements of organizations and cloud service providers, who can employ them prospectively or retroactively. They utilize UEBA tools to help discover the underlying cause of seemingly unrelated issues as they arise, or they use them after a failure to determine whether there were warning signs of the problem that might have been detected sooner - and will be if the problem recurs.

  • Hardware and Software Defects: Anomalous behaviors may signal a present or imminent hardware, operating system, middleware (such as database management systems), application server, or application problem. A rising number of data transmission problems on a particular network switch port, for instance, might signal that the hardware is malfunctioning or that there is an issue with the port's cabling.

What are the Best Practices For UEBA?​

After an organization has installed and activated its UEBA system, it may use several principles to assure the system's optimal performance. The following security best practices apply to UEBA and other security solutions. Ensuring that all workers have the required knowledge to execute security, and how a business utilizes these technologies, is a crucial aspect of enhancing security.

  • Privilege Escalation: Numerous businesses overlook the dangers presented by user accounts without privileges. Attackers often target low-access accounts and utilize them to elevate their privileges and compromise vulnerable systems. A UEBA system aids in the detection of illegal attempts to elevate privileges. The program should be set to notify the security team of occurrences involving privilege escalation.

  • Anticipating Internal Threats: This best practice is unique to UEBA systems; while designing rules and policies to detect attacks, businesses must evaluate their complete threat profile. UEBA's capacity to identify insider threats as efficiently as it detects exterior threats is one of its main benefits. Nevertheless, the identification of insider threats is only achievable if the system is equipped to check for them.

  • UEBA Baselining: Business security objectives and appropriate user behavior are crucial baselining concerns. This period should be neither too long nor too short; if the baseline period ends too soon, there may not be enough time to gather correct data, and the rate of false positives may rise. On the other hand, if an organization takes a long time to establish a baseline, some malevolent actions may be mistaken for typical organizational behavior. Because user and entity activity is continually changing, it is necessary to periodically update baseline data. Employees can move positions, projects, and privilege levels, allowing them to engage in new tasks. Organizations may set up their UEBA systems to automatically gather data and alter baselines as conditions change.

  • Access Control: To safeguard their UEBA systems, firms must provide the proper rights to the proper employees. The UEBA system should not be accessible to everyone. Only authorized team members should be able to read security information, and the system should only alert these people.

  • Training: Workers must have the knowledge and skills required to run UEBA systems for their effective use. A security note template may assist workers in comprehending the significance of security. Throughout the year, it is essential to promote security knowledge and best practices; this applies to UEBA systems and other forms of security software.

  • Using Other Security Tools: Do not replace UEBA procedures and technologies with fundamental monitoring solutions like intrusion detection systems (IDS). UEBA systems are not a substitute for conventional monitoring infrastructure, but rather a supplement.

What are the UEBA Tools?​

As technology advances, the market for user behavior analytics products continues to expand and develop. The following are some of the UEBA products currently available on the market:

  • Aruba IntroSpect: IntroSpect is an integrated UEBA and Network Traffic Analysis (NTA) system that uses machine learning to identify, prioritize, analyze, and react to stealthy inside threats that bypass conventional perimeter security measures. It has the following additional attributes:

    • Integrates with Aruba ClearPass NAC to perform policy-based enforcement steps automatically to prevent attacks (quarantine, port block, etc.)
    • Collects and analyzes all data, including packets, flows, logs, and warnings.
    • Adaptive machine learning models to attack families like ransomware
    • Identifies attacks in gestation from hostile, careless, or compromised humans, IoT devices, and systems.

  • Cynet 360 AutoXDR: Cynet XDR is a comprehensive solution for breach protection. It provides businesses with a unified, multi-tenant platform that can consolidate endpoint, user, and network security capabilities into a single suite. As part of the company's services, CyOps, Cynet's 24/7 SOC team of threat researchers and security analysts, is provided. Cynet XDR prevents and identifies endpoint, network, and user risks. Cynet Sensor Fusion offers integrated antivirus, endpoint detection and response, network analytics, deception, and user behavioral analytics of the next generation. Cynet Response Orchestration is a collection of remedial operations designed to handle infected hosts, malicious files, network traffic controlled by an attacker, and compromised user accounts. CyOps assists with in-depth investigations, proactive threat hunting, malware analysis, and attack reports, ensuring that every security incident is rectified.

  • Exabeam: Advanced Analytics from Exabeam is included in its Fusion SIEM service. Using behavior analytics, Fusion SIEM eliminates silos by integrating weak signals from several products into high-fidelity threat indicators. This method identifies complicated, unknown, and insider threats to identify attacks overlooked by purpose-built or other analytics tools. The system integrates SIEM, UEBA, and XDR into a single offering. Fusion SIEM has centralized data storage, compliance reporting, and a quick search engine. Fusion SIEM may be used in combination with current solutions. There are pre-built interfaces with hundreds of third-party security solutions. Behavior analytics combines weak signals from numerous items to identify sophisticated risks.

  • Fortinet FortiInsight: Using automated detection and response capabilities, Fortinet's UEBA technology protects companies against insider threats by continually monitoring users and endpoints. Using machine learning and sophisticated analytics, FortiInsight detects non-compliant, suspicious, or atypical activity and immediately notifies administrators of any compromised user accounts.

    This technology is a key aspect of FortiInsight. Fortinet purchased ZoneFox and incorporated it into FortiInsight. When linked with FortiSIEM as part of the Fortinet Security Fabric, it gives insight into data activities and decreases the risk of insider attacks and compliance challenges associated with regulations such as GDPR and HIPAA. It comprises endpoint behavioral monitoring of devices even when they are not connected to the corporate network, as well as tracking of all accessible resources. A rule-based engine detects policy violations, illegal data access, data exfiltration, whether data is being sent to the cloud via a local USB drive, and compromised accounts.

  • Rapid7 InsightIDR: InsightDR by Rapid7 continually baselines regular user behavior beyond predefined signs of compromise to identify difficult-to-detect attacks, such as attackers masquerading as corporate workers. This UEBA tool associates network activities with certain users. When a user exhibits strange behavior, the reaction and inquiry are swift. InsightDR automatically associates network activity with the corresponding people and organizations. The solution continually establishes user activity baselines, responding to users and network components to define normal. Every InsightIDR warning automatically highlights noteworthy user and asset activity on a visual timeline, allowing IT to choose where to concentrate its effort. The dashboard has three boxes to display dangerous users, a watch list to monitor users who may represent a greater risk, and ingress locations to display where in the globe people are authenticating to your systems. The system identifies misconfigurations using a visual log search and pre-built compliance cards that identify irregularities.

  • Fortscale: Fortscale is an expert in user behavior analytics, particularly analytics tailored to combat insider threats. It provides two solutions: Fortscale UEBA for SOC, which is targeted for deployment in security operations centers, and Fortscale Presidio, a UEBA engine that other security manufacturers may include in their products. It has received $39 million in fundraising, including a $7 million round that concluded in February 2017. It was founded in 2012 in Tel Aviv, Israel. Blumberg Capital, CME Ventures, Evolution Equity Partners, Intel Capital, and Valor Capital Group are important investors. It has the following additional attributes:

    • Integration with DLP and other security technologies Multivariate risk scoring
    • Alert transmission Hadoop-based investigation of the Darknet Agentless
    • Single-click research capabilities
    • Smart alerts

  • LogRhythm: LogRhythm UEBA identifies known and unknown user-based risks using analytics, combining machine learning and scenario analytics to surface and rank significant events. As a standalone UEBA product or as an add-on to current SIEM or log management systems, this enhances organizational security settings. It has the following additional attributes:

    • Security orchestration, automation, and response embedded
    • Evidence-based investigational starting points
    • LogRhythm TrueIdentity creates complete behavior profiles that quantify and rank the risk associated with aberrant user conduct.
    • Automated user profiling and risk assessment

  • Microsoft Sentinel: Microsoft Sentinel gathers logs and alerts from all of its linked data sources, analyzes them, and generates baseline behavioral profiles of your organization's entities (including users, hosts, IP addresses, and apps) across time and peer group horizons. Microsoft Sentinel can then spot aberrant behavior and assist in determining whether an asset has been hacked using a range of methodologies and machine learning capabilities. In addition, it can determine the relative vulnerability of certain assets, identify peer groups of assets, and assess the possible effect of a compromised asset (its "blast radius"). You may properly prioritize your investigation and incident management using this information.

  • One Identity Safeguard for Privileged Analytics: One Identity provides solutions for identity management, access control, and privileged account management. Using user behavior analytics technology, One Identity Safeguard for Privileged Analytics detects high-risk privileged users, analyzes suspicious actions, and discovers risks. It offers a complete insight into privileged account users' activities. Organizations may identify hazardous users, be vigilant for emerging internal and external risks, and spot out-of-the-ordinary privileged conduct. Safeguard allows IT security professionals to take rapid action and prevent possible data breaches if suspicious behavior is found.

  • Palo Alto Cortex XDR: Cortex XDR was created by Palo Alto Networks as a detection, investigation, and response application that natively combines network, endpoint, and cloud data. It identifies risks using behavioral analytics, speeds up investigations using automation, and prevents attacks before they do harm by integrating tightly with current enforcement points. It has the following additional features:

    • Risky user behavior analysis
    • Prevention of malware, ransomware, and exploits
    • Automated examination of alerts and root cause analysis
    • Customized rule-based attack behavior detection
    • Specific attack detection
    • Detection of malware and fileless attacks
    • Internal threat identification
    • Threat hunting
    • Incident response and recovery
    • IoC and threat intelligence
    • Analyzes of post-incident impacts

  • RSA NetWitness UEBA: RSA NetWitness UEBA is an integral component of the RSA NetWitness Platform. It is a purpose-built, big-data-driven, user and entity behavior analytics solution. It allows the identification of unknown threats based on behavior without requiring analyst adjustment by utilizing unsupervised statistical anomaly detection and machine learning.

  • Securonix Bolt: The most current offering from Securonix, the SNYPR Security Analytics Platform, includes SIEM, UEBA, and fraud detection features. Bolt, however, is a standalone UEBA solution offered by the firm. The firm was created in 2008 and has offices in Addison, Texas; San Francisco, California; Jersey City, New Jersey; Los Angeles, California; Atlanta, Georgia; Vienna, Virginia; the United Kingdom; and India. One-third of Fortune 500 organizations, according to Securonix, employ its products. It has the following additional features:

    • Capabilities for investigation and reaction.
    • Fraud Reporting
    • Patient data analytics
    • Adaptive and predictive learning
    • Library of Threat Model Exchange
    • Trade Surveillance
    • Visualizations
    • Compatible with the SNYPR Security Analytics Platform Agentless
    • More than 1,000 threat models are installed with a single click across 350 connections.

As the industry continues to consolidate, top vendors are increasing including UEBA and UBA capabilities in their entire cybersecurity offerings.

What are the Differences Between UEBA and UBA?​

User Behavior Analytics (UBA) was first described in 2014 as a category of cybersecurity products that utilize advanced analytics to identify abnormalities and malicious behavior while analyzing user behavior on networks and other systems. These may be used to detect security risks such as malevolent insiders and compromised privileged accounts that conventional security technologies cannot detect.

UEBA varies from User Behavior Analytics (UBA) in that UEBA's moniker contains an additional "E" for entities, devices, and apps. UEBA incorporates the monitoring of nonhuman processes and machine entities, such as servers, routers, and endpoints. It is a more inclusive form of UBA.

Gartner introduced the letter "E" in October 2017 to assist the security industry to recognize that entities other than individuals must be profiled to detect risks more precisely. Because devices are linked to routers, it is obvious that user and entity activities are coupled. Managed and unmanaged endpoints, applications (including mobile, servers, and on-premises apps), networks, and threats themselves must also be monitored.

The proliferation of the Internet of Things (IoT) devices that create vast quantities of sensor data was a key factor in the transition from UBA to UEBA. This data may be analyzed by UEBA, which can examine IoT devices individually or in peer groups to detect abnormal activity that may indicate hostile intent.

UEBA may also be used to monitor a huge number of cloud assets that are constantly supplied and remotely used, making them challenging to examine with conventional security technologies. UEBA may examine cloud-based assets to determine if their collective behavior is normal or abnormal.

Consequently, entity, or the letter "E," is considerably more inclusive, as is UEBA in comparison to UBA.

What are the Differences Between UEBA and NTA?​

Typically, UEBA solutions are sold as bundled offers or as components of established security products, such as cloud access security brokers (CASBs) and detection and response platforms. They function by assessing network user and other entity activities, including hosts, applications, data repositories, and network traffic. Using real-time and historical data, they utilize machine learning to establish a baseline for typical activity.

After establishing this baseline, UEBA systems use a variety of analytics techniques, such as basic statistics, pattern matching, and signature-based rules, to search for abnormalities that may signal potentially hostile or suspicious activities. For UEBA systems to properly execute behavior analytics, an organization must first possess a rich and integrated data set for machine learning technologies.

Applying machine learning and behavioral analytics to humans, devices, and entities, UEBA systems can identify insider threats, malware, and sophisticated attacks. They give the knowledge necessary to detect aberrant behavior in real-time and provide investigative insights so analysts can swiftly confirm and eliminate risks before they cause harm.

NTA systems use machine learning, sophisticated analytics, and rule-based detection to monitor and analyze all business network traffic and flow records in order to detect prospective attacks, insider abuse, suspicious behavior, and malware. This involves monitoring and analyzing all east-west communications from network sensors and all north-south traffic through the business boundary.

The main difference between NTA and UEBA is that NTA enables businesses to see all events, not just those that have been recorded, throughout their entire network. This comprises every facet of the activity of a cyberattacker. Similarly to UEBA, NTA allows businesses to profile both user accounts and network devices, and its deployment is quite simple.

A company with more complex security demands would likely need both NTA and UEBA solutions. Unlike UEBA, NTA cannot follow local events, such as those from a device that is not connected to the network and is typically incapable of identifying more sophisticated security threats.

What are the Differences Between UEBA and SIEM?​

Security information and event management (SIEM) is the use of a sophisticated combination of tools and technologies that provides enterprises with an all-encompassing picture of their IT security system. Utilizing data and event information, it provides insight into regular patterns and sends warnings when unexpected conditions and occurrences occur. SIEM is similar to UEBA in that it utilizes information about user and object behavior to determine what is deemed normal and what is not.

SIEM is an ideal starting point for security monitoring and analytics since it collects information from firewalls and operating system and network traffic logs. This naturally raises the issue of whether an organization would need both SIEM and UEBA.

While SIEM and UEBA may seem remarkably similar, they perform distinct functions.

SIEMs are effective security management tools, but their threat detection and response capabilities are less developed. SIEMs can readily manage real-time threats, but they are incapable of detecting a sophisticated cyberattack. This is because competent cyberattackers skip simple, one-off attacks in favor of prolonged attacks that might go unnoticed by conventional threat management solutions for weeks or even months.

UEBA is a significant improvement over UBA and conventional SIEM systems for many reasons. First, it overcomes the constraints of SIEM correlation rules and the fact that, in many instances, the correlation rules paradigm as a whole is flawed. Among the issues connected with depending on SIEM correlation rules are the following:

  • Rules demand too much upkeep.

  • Security teams are unable to detect attacks because the criteria lack context or overlook instances that have never occurred before, hence producing false negatives.

  • Improperly filtered rules might impede the execution of incident response procedures. Because administrators must filter the application of rules to identify which data is relevant and which is irrelevant in your event landscape,

Additionally, UEBA lowers false positives, hence reducing alert fatigue. And by allowing teams to prioritize their warnings, UEBA enables your security specialists to concentrate on the signals that pose the most danger and are the most trustworthy.

Moreover, UEBA systems are capable of identifying more complex threats, such as those that may be undetected daily but exhibit an unexpected pattern over time. Malvertising is an example of this, an innocent advertising applet that captures user data or infects a user's device after being downloaded to a browser.

By combining UEBA with SIEM capabilities, businesses are better equipped to protect against a broad variety of threats. By concentrating less on system events and more on particular user or entity behaviors, UEBA creates a profile of an employee or entity based on use patterns and provides an alert if it detects suspicious or unusual user activity.

SIEM excels in compliance reporting and monitoring of events, such as access activity, UEBA is superior at detecting insider threats and protecting an organization's digital assets, particularly when those assets include high-value intellectual property.

You would not want to use both systems simultaneously. Before turning to a UEBA solution, if you have previously installed a SIEM system, review its user monitoring, profiling, and anomaly detection features to decide if they can be tailored to your use cases.

Both SIEM and UEBA offer key features that help businesses to achieve their security and business requirements. Because insider attacks are real and expensive, UEBA should be considered as a SIEM supplement.

UEBA capabilities are merging with those of other technologies, enabling SecOps teams to identify possible security risks with superior and more sophisticated analytics. Gartner expects that the standalone UEBA market will cease to exist shortly. Instead, it will be used to upgrade SIEMs and other tools with sophisticated analytics by directly incorporating UEBA capabilities and functionality into their platforms. Gartner essentially urged vendors to integrate UEBA solutions into SIEM systems and sell them jointly in 2017. Some of the providers, like Exabeam, responded to this request and offer their SIEM products with UEBA and security automation features.

What are the Differences Between UEBA and XDR?​

UEBA capabilities are merging with a new class of threat detection and response technologies known as XDR. The "X" in XDR represents any data source, whether a network, endpoint or cloud. XDR arose as an extension of endpoint detection and response (EDR) solutions in response to the demand for threat visibility that was beyond that of a SIEM but was not restricted to endpoints. XDR accelerates investigations and increases the productivity of security operations team members via the use of automation.

XDR integrates the capabilities of EDR, UEBA, NTA, next-generation antivirus, and other technologies into a single solution for the highest level of protection. It does this by delivering businesses with the visibility and behavioral analytics they need across their entire infrastructure, including networks, clouds, and endpoints, to detect, track down, analyze, and react to possible security risks.

What is the Future of UEBA?​

Several years ago Niara was acquired by HPE-owned Aruba, Balabit was acquired by One Identity, E8 Security was acquired by VMware, and Fortscale was acquired by RSA. Gartner anticipates that this tendency will intensify, with the UEBA business largely disappearing by 2021. Gartner anticipates that 80% of threat detection and incident prioritization systems will include fundamental UEBA methodologies and technologies. Gartner forecasts that UEBA will be replaced by more comprehensive security analytics technology in the long run.

SIEM systems are the most apparent destination for UEBA technology. Gartner anticipates a significant convergence between the two, with all key SIEM suppliers currently delivering UEBA capabilities via the development of their UEBA technology, integration with other UEBA solutions, or partnership with a UEBA vendor. Some UEBA providers, like Exabeam and Securonix, have added SIEM capability to their feature sets.