There is no organizational network with a security solution comprehensive enough to keep out all modern cyberthreats. It's common to hear the phrase "It's not a matter of if, it's a matter of when," in cybersecurity. While it might seem like a cliche, it's true. Your business might never be the victim of a successful cyberattack, but it will eventually unwittingly host a cyberattacker who successfully breached your network. The reason is not necessarily inadequate security, it's because today's hackers have learned how to exploit unknown vulnerabilities, user errors, default settings, and human nature. Most attacks begin when an attacker finds an entryway into your network or gets invited in by one of your employees.
Some of the top cyber threats to watch for in 2023 include phishing, business email compromise, credential theft, and social engineering. Those types of attacks sound tame in comparison to the major ransomware attacks that shut down oil pipelines and halt food production, right? The truth is, most major attacks like ransomware and high-profile data breaches begin with a seemingly lower-level attack that allows a bad actor into your network.
All of the major companies that suffered expensive ransomware attacks in 2021 had some type of cybersecurity system that attackers managed to infiltrate before launching the software that shut down the system. It's possible that the attackers even spent days moving quietly through the network to prepare for the perfect attack. In today's modern threat landscape, you need more than a lock and an alarm on the outside of your network. You also need high-level security guarding the activities that occur within your work environment.
This is where SIEM and UEBA enter the picture. While it's true that cyberattackers are gaining more technological knowledge and gaining more sophisticated threat tactics every day, cybersecurity technology is advancing just as rapidly. Cybersecurity software is designed to always be one step ahead of modern attackers to anticipate malicious actions, and stop them before damage occurs. SIEM and UEBA are some of the most modern advances in cybersecurity designed to detect suspicious actions before they become expensive attacks.
To understand how UEBA can improve your organization's security posture and protect against sophisticated cyberattacks, it helps to learn how SIEM and UEBA perform and the actions they take to protect your network. This guide will help you understand the technology behind SIEM and UEBA and how they work together as a crucial part of your complete cybersecurity solution.
Understanding SIEM UEBA
SIEM is the system that collects and categorizes the data used in your security operations. UEBA uses this data to perform essential analyses that help security professionals detect and respond to insider threats. It's important to note here that insider threats aren't only those devised by employees who use your network. The term is used to describe any entity that has gained entry to your network with an intent to do harm. Most often, insider threats are attackers moving discreetly through your network to gain higher access levels or obtain sensitive data. Learning about how SIEM and UEBA work can help describe how these tools detect and respond to inside threats.
What is SIEM?
Security Information and Event Management (SIEM) is a tool that collects data used to investigate security incidents and respond to real-time cybersecurity events. Your SIEM system collects log and event data produced from applications, devices, networks, infrastructure, and systems to provide complete visibility into the activities that take place across your network. While many legacy SIEMs were on-premise solutions, Next-Gen SIEM solutions are cloud-based to better protect hybrid and cloud-based environments. When properly optimized to your network environment, your SIEM can collect and analyze massive amounts of data in real-time and use machine learning algorithms to detect advanced threats and provide AI-based security incident response capabilities.
In other words, your SIEM system constantly records and analyzes all the activities that occur within your network. When unusual actions take place, the system sends an alert to announce suspicious (potentially malicious) behavior.
What is UEBA?
User and Entity Behavior Analytics (UEBA) is a security solution that uses machine learning to understand the normal activity that occurs in your network. The system uses security analytics to build profiles that model standard behavior for each user and entity that is connected to the network. After establishing a baseline of normal behavior, the system can detect when an authorized user is performing suspicious activities.
As you can imagine, when a SIEM collects massive amounts of data, there could be thousands of activities that occur each day that could be flagged as suspicious. This is where a SIEM that isn't properly optimized or aligned with other tools can fall short by sending out a deluge of false alerts. In order to eliminate this problem, analysts must constantly feed new information to the SIEM to weed out false alerts.
UEBA adds context to the information by identifying each user and providing information about authorization levels, location, permissions, etc. This additional information allows the system to identify the user and determine whether the behavior in question is suspicious in relation to their permissions level and typical workflows. Instead of simply assuming the behavior of an authorized user is normal, the system can now raise an automated alarm that says the behavior is suspicious for a specific user. With this advanced capability, the system provides alerts to signal an occurrence that suggests credentials may have been compromised or accounts hacked.
How UEBA Works in Conjunction with SIEM
SIEM collects and categorizes the data that comes from all devices within your network. It uses machine learning to determine which information requires further analysis and sends that data to your security team. UEBA adds context to SIEM information that is critical for detecting unusual behavior based on user and entity signatures. So, exactly how do the two work together?
Many security vendors offer SIEM systems and UEBA systems that are designed to complement each other. SIEM uses rule-based threat detection while UEBA complements that detection with self-learning threat detection. UEBA is so critical to effective SIEM performance that Gartner views UEBA as a SIEM capability by which vendors are evaluated in the Magic Quadrant for Security Information and Event Management.
While most vendors provide UEBA as a separate security component, Securonix SIEM integrates UEBA into the system for a complete end-to-end platform that can quickly be deployed and provide value. When SIEM and UEBA converge, they can use an entity as a correlation point to tie related events together into a threat chain that tells the story of an insider attack. As a unified solution, the SIEM UEBA presents the chain of connected events to the analyst without requiring the manual task of sending information from one system to the other to provide a complete picture.
How SIEM UEBA Can Help You Achieve Better Cybersecurity Posture
SIEM uses rule-based threat detection that is tailored to meet specific analytics. UEBA uses self-learning threat detection and unsupervised machine learning to enhance the information the SIEM provides. While neither solution can replace the other, the tools work in tandem to provide the most comprehensive visibility into your network available. When SIEM analytics are improved with contextual data from UEBA, your cybersecurity solution is vastly improved with these improved capabilities.
Automated Detection of Insider Threats
Attacks like phishing, BEC, credential theft, and account takeover make insider threats one of the most dangerous activities in your network. Insiders are considered authorized users, but they can still represent a threat. There are three types of insider threats.
- Negligent Insider: An employee or network user with privileged access who doesn't follow proper IT procedures, can be a threat. By failing to use proper security measures, this user is leaving the network open to attack.
- Malicious Insider: A malicious insider is an employee or contract with privileged access to a system that they intend to use for malicious activity. It's what most people think of as an insider attack because it's an attack actually performed by an authorized user.
- Compromised Insider: This is where those sophisticated attacks come into play. A compromised insider appears in the network as an authorized user who appears to be carrying out typical network tasks. However, this is an attacker who has gained the credentials of an insider.
Traditional security tools treat authorized user actions as routine behavior. When UEBA adds user context to event data, it becomes clear when authorized users are behaving in suspicious ways. Unified use of SIEM and UEBA correlates these suspicious actions into a threat chain that can easily be identified as an attack pattern. This automated detection allows the system to send out alerts and even automated response actions to stop an insider threat before it becomes an advanced attack that reaches a damaging objective.
For instance, an attacker who breaches the system through a phishing attack or stolen credentials may initially appear as an authorized user. However, when this authorized user attempts to gain elevated permissions or create a privileged user account, UEBA recognizes the action as suspicious behavior. Alerts are also raised for an abnormal number of failed logins or an authorized user login that comes from an unusual device or location.
A SIEM system is flooded with information from multiple security tools and critical systems. If each of these pieces of data carries the same level of importance, critical information could go unnoticed until it is too late. Furthermore, without incident prioritization, a massive amount of security alerts will be generated that demand the attention of security staff for investigation. A large number of irrelevant alerts leads to alert fatigue which can mean analysts become numb to actual threats.
The biggest challenge that enterprises are facing today is that analysts get too many alerts. Even when alerts are enhanced with contextual data, not all suspicious behavior is created equal. Effective UEBA continually ranks suspicious activity by risk level. This is accomplished by adding context from your organization that describes the criticality of assets and the access levels of specific functions and users. With this additional information, major deviations from normal behavior get a higher risk rating than a small change from the norm.
Data Leak Prevention
Authorized users within a business are required to handle, store, and transfer data every day. Approving each of these actions individually would slow workflows to a crawl and require additional resources and personnel. Similarly, legacy data loss prevention tools report on any potentially unusual activity carried out on sensitive data. Since practically all information holds some level of value, this can lead to a high volume of alerts.
Large data transfers are an indicator of a data breach. Yet, many malicious activities surrounding data loss might not be this noticeable. UEBA solutions can use known user and entity baselines to understand and categorize which events represent anomalous behavior. When this data is accurately prioritized, investigators can discover real security leaks faster.
Enhanced Endpoint Protection
Before the term UEBA was developed, contextual analytics tracking was referred to as UBA (user behavior analytics). However, entities, or IoT devices that are connected to your network also pose crucial security risks. Now, UBA is expanded to UEBA to also track the events of devices (entities) connected to your network. Monitoring devices in the age of Industry 4.0 is essential for a variety of reasons.
- Hackers can and do implant malware into devices to provide a backdoor into networks and databases. IoTs are typically designed for convenience over security, which means default passwords might be mass-produced and never changed, making them an easy target.
- Threat actors that obtain remote access to devices can essentially transform them into bots that allow hackers to communicate through and take control of devices.
- Remote devices are more susceptible to theft. If multiple authentication protocols are used, these devices can be a means to avoid detection.
In the same way that UEBA provides contextual information for users, it analyzes essential device information to detect activities that fall outside the normal realm of activity for that device. This early detection can be used to automate responses that isolate a threat to the specific device before access to the network is achieved.
Targeted User Education
User behavior is an essential part of effective security. All too often, an organization's employees don't have a complete understanding of the components of good cybersecurity hygiene. When network users don't understand how to effectively protect sensitive data or even don't recognize that their actions are putting the company at risk, they can't be responsible for inviting threat actors in.
UEBA tracks user activities, including those of negligent users. It can identify and address risk-related human behaviors associated with phishing, data transfer, access control, and poor password practices. When employers know which users need additional training, they can provide targeted training to address specific behaviors and avoid future actions that could lead to successful attacks.
In order to take in all relevant information and eliminate the chance of missing critical threats, SIEM systems take in a huge amount of information. Only as these systems are constantly tuned to rule out false alerts, are they capable of streamlining the information provided to analysts to a manageable level. As real threats increase, false alerts become more cumbersome than ever, overwhelming security staff with irrelevant noise. Catching up on a backlog of alerts puts security professionals in a position to relevant attacks.
UEBA uses machine learning to apply context to every alert, automatically decreasing false alerts. Instead of spending large amounts of time manually feeding new information into SIEM systems, security teams have more time to focus on activities representing the most significant risk. By prioritizing risk, response times to relevant threats are significantly decreased.
Rapid Time to Value
While SIEM is a critical tool for effective modern cybersecurity, optimization of a SIEM system is no easy task. Without optimization, your SIEM system is basically a blank slate that considers many routine activities potentially suspicious. Since SIEM works on rule-based learning, new information must be routinely added to "teach" the system why certain activities are normal behavior.
When UEBA works with SIEM, you can immediately add contextual data to the information collected by your SIEM. This context enriches the information to automatically teach the SIEM what behavior is normal by establishing a baseline. By eliminating the bulk of false alerts, your organization realizes quicker time to value with an accurate alert and response system.
Improved Visibility of a Remote Workforce
Remote and hybrid work is more prevalent than it has ever been before. In many industries, this structure is largely successful in improving employee satisfaction and maintaining production. However, remote work does increase organizational vulnerabilities. Cloud-based, Next-Gen SIEM with UEBA can extend critical protection to the devices used for remote work.
UEBA can improve business security in remote and hybrid work environments by establishing baseline behavior for remote workers and devices. Geolocation tools can provide alerts for logins that occur in a non-typical location to potential risks like compromised credentials. Since remote devices are more susceptible to attack, UEBA can provide a crucial extra layer of protection to trigger alerts of brute force attacks, data breaches, and changes in permissions or the creation of superusers.
Reduced Manual Labor for Analysts
UEBA provides a significant amount of contextual data to drastically improve the level of information that is supplied with alerts. When UEBA works along with SIEM to provide automated alerts and responses, it reduces the amount of manual data input typically required by analysts for effective SIEM tuning. UEBA depends on machine learning instead of rules-based logic, and can automatically introduce new data to your SIEM. Your IT staff responsibilities can be reduced even further when you invest in managing SIEM that is monitored by a third party MSSP.
Managed Securonix SIEM With UEBA From BitLyft Offers Superior Protection for the Modern Enterprise
Today's sophisticated attackers are financially motivated to launch attacks that can cause severe damage to large organizational networks. To accomplish this, attackers need time to investigate the network and elevate their journey to access the most protected levels of information. These attacks are typically conducted over the course of weeks or months to reach a successful objective without detection. Monitoring your entire environment with the use of Next-Gen SIEM combined with effective UEBA provides you with an opportunity to detect and respond to these attacks before damage occurs.
Next-Gen SIEM from Securonix uses the most advanced technology to protect hybrid and cloud-based environments with analytics-based threat detection and response. Cloud-based to collect and parse massive volumes of data in real-time, Securonix uses patented machine learning algorithms to detect advanced threats and is the only SIEM that is integrated with UEBA and the MITRE ATT&CK framework.
Securonix SIEM is focused on security analytics and machine learning for threat detection. The integration of UEBA and the MITRE ATT&CK tactics, techniques, and procedures allows the SIEM system to automate the prioritization of the highest risk threats by correlating events to eliminate redundant alerts. By using contextual data and using automated detection based on stages of an attack instead of only anomalies and alerts, organizations are more likely to achieve early detection of threats lurking in the network.
Managed Securonix SIEM from BitLyft offers premium protection for your network with the most comprehensive modern protection available. Our experienced security team installs and optimizes your SIEM to provide greater visibility to your log and event environment. Next-Gen SIEM combined with cybersecurity expertise combats APTs with the most up-to-date talent and technology. Schedule an appointment with BitLyft to learn more about how Securonix SIEM with integrated UEBA can help you achieve your security goal.