User and Entity Behavior Analytics (UEBA) is a long-term that essentially refers to a security process that utilizes analytics to identify abnormal network behavior.
UEBA takes a proactive approach by scanning the actions of users and entities within a network. It doesn’t just react to threats but also establishes a baseline from which it learns to be more precise and timely, empowering you to stay ahead of potential security breaches!
Any deviations from the organization’s network activity baseline could signal malicious activity, such as compromised accounts inside the company or a brute force attack outside the network. UEBA systems continuously accommodate changes in normal network behavior and become better at identifying and treating abnormal behavior.
What is User and Entity Behavior Analytics (UEBA)?
UEBA technology helps organizations find threats by using machine learning to monitor and analyze network behavior. UEBA systems use pattern matching to detect anomalies that could indicate security threats. A UEBA system can protect organizational assets, customers, and reputation when used hand-in-hand with administrative teams and organizational policy.
How does UEBA work?
UEBA collects, processes, and analyzes users’ and entities’ network activity to establish a baseline behavioral reading. After the baseline behavior is set, the algorithm identifies user and entity behaviors that exceed or fall below the determined baseline thresholds. These abnormal behaviors automatically alert system administrators and security teams in real time, providing a sense of security and confidence in the system’s capabilities.
Detecting an advanced attack using an employee’s compromised credentials is a practical example of UEBA. Suppose a threat actor uses the employee’s credentials to access the network from a different IP address or begins transferring large data packets outside of typical employee transfers. In that case, a UEBA solution will alert and, depending on capabilities, block the actions, lock out the user, or report a false positive as needed.
UEBA Use Cases
UEBA is a practical solution used across various industries to enhance security and operational efficiency. Its critical use cases include detecting insider threats by identifying deviations from typical user behavior, preventing data breaches by spotting unusual access patterns (like compromised accounts), and automating risk responses to mitigate potential security incidents quickly.
1. Detect Insider Threats
UEBA tools’ pivotal use case is detecting insider threats, as these threats often originate from within. UEBA systems continuously monitor and analyze user activities across the corporate network, including login sequences, file access histories, and standard usage patterns with slight deviations.
For example, suppose an employee accesses sensitive data at an unusual time or downloads large volumes of data that do not align with their normal job functions. In that case, UEBA tools can flag these actions as potentially malicious.
Furthermore, UEBA can integrate with other security tools so that alerts automatically trigger response protocols when suspicious activity is detected, like temporarily restricting user access.
2. Detect Compromised Accounts
UEBA’s critical application is to detect compromised accounts, which helps organizations safeguard operations. For instance, if a user account shows unexpected login attempts from foreign locations or atypical times, these are flagged as suspicious. UEBA tools thus distinguish between legitimate user actions and potential security threats.
Integrations with access management tools allow direct treatment of compromised account risks. When a potential account compromise is detected, a robust UEBA system can automatically enforce additional authentication requirements while alerting admins to pay real-time attention.
3. Detect Brute-force Attacks
UEBA has a wide range of use cases in its defense of network integrity, including brute-force attacks. These attacks involve frequent, repeated login attempts using structured passwords to gain unauthorized account access. UEBA systems monitor login activities across the corporate network to track failed login attempts’ frequency, timing, and location.
A frequent brute force attack involves failed login attempts from a single IP address within a short time. UEBA tools can flag these logins as suspicious, allowing timely intervention to prevent unauthorized access. When a potential brute-force attack is detected, UEBA systems can automatically trigger security responses, such as locking the affected accounts and thwarting the potential attack while minimizing data breach risk and further network compromise.
4. Detect Changes in Permissions & Creation of Super Users
Detecting changes in permissions and creating superusers prevents unauthorized access escalation within a network. UEBA systems meticulously monitor user roles and permission changes, flagging unusual activities as indicators of malicious intent: an insider attempting to gain elevated access to sensitive data or an external attacker using a compromised insider account to create a backdoor, for example.
This UEBA capability is enhanced through integration with identity and access management (IAM) systems, centralizing all permission-related activities under one view and, most importantly, unauthorized attempts to modify access rights.
Suppose a UEBA tool detects that a low-level user account suddenly grants itself administrative privileges or creates a new super user profile during off-hours. In that case, it can initiate predefined response protocols, such as revoking the changed permissions.
5. Detect Breach of Protected Data
Safeguarding sensitive information from unauthorized access and exfiltration must be an organization’s top priority, mainly when critical IP is at stake. UEBA tools monitor and analyze user access to restricted or sensitive data, such as financial records, personal employee details, or intellectual property.
If a user who typically accesses specific types of documents suddenly starts accessing sensitive files, UEBA tools can immediately flag these actions as suspicious.
Integrating UEBA with data loss prevention (DLP) systems significantly enhances data breach detection and mitigation. When a UEBA system identifies an anomaly, such as access to sensitive data at odd hours, it alerts and enforces based on predefined security protocols. Integrating with a DLP system provides more holistic data security with behavioral insights and data movement controls.
Benefits of UEBA: Why Companies Need It
Regardless of industry or organization type, UEBA can deliver significant security benefits, particularly to companies dealing with large, sensitive datasets – think finance, healthcare, and government organizations.
By leveraging UEBA, companies gain an added layer of customized detection for insider threats, compromised accounts, unauthorized data access, and sudden external attacks. These are all faster and more accurately executed than “manual” systems can afford, enhancing their overall security posture.
Address A Wider Range of Cyberattacks
As evidenced in the above use cases, UEBA can be an invaluable tool for addressing internal and external threats. Unlike traditional security measures that often focus on external threats, holistic UEBA systems offer a deep view of user behaviors, enabling the detection of subtle signs of compromise.
For example, UEBA software can analyze and cross-reference different data points in the early detection of advanced attacks such as Advanced Persistent Threats (APTs), ransomware, and phishing schemes where typical signature-based security tools might fail.
UEBA can alert to unusual network connections or an abnormal increase in database read volumes, behaviors often associated with APTs scouting for valuable data. Additionally, sudden changes in file permissions or the creation of backdoor accounts – classic ransomware signs – can be swiftly flagged and mitigated.
Require Fewer IT Analysts
UEBA can significantly enhance the efficiency of IT security operations, allowing organizations to operate with fewer IT analysts while maintaining or improving their security posture. By automating the detection and response to unusual behaviors within the entire network, UEBA reduces the manual workload traditionally placed on IT security teams.
This data-driven “automation” helps preemptively reduce the time analysts need to spend on initial data gathering and analysis, allowing for resource allocation to more strategic tasks such as incident response planning and cybersecurity policy development. In parallel, UEBA also contributes to more efficient IT policy management: using established baselines bolsters the identification of necessary updates or changes in security protocols.
Reduce Costs
Organizations need a powerful tool to enhance their cybersecurity defenses. The right tool can lead to cost savings in several ways. When implemented with both outcomes in mind, an effective UEBA system prevents costly security incidents and optimizes security team operations.
Imagine security analysts using UEBA alerts and incident insights to move from awareness to solution in a more compressed timeline. Labor costs related to incident management would naturally decrease, and the probability of dollar damage caused by the incident would also decrease.
A tighter, UEBA-led security system also significantly reduces indirect costs—regulatory body fines, legal fees, and compensation to affected parties or customers. Fewer security incidents mean avoiding the costly downtime that tends to follow a significant security breach, including the distractions associated with delving into the incident with expensive legal and third-party teams engaged.
Lower Risk
UEBA addresses several risks associated with network security risk, first and foremost the insider threat: trusted employees or contractors misuse access to steal data/IP or sabotage critical systems.
UEBA also effectively detects compromised accounts (the #2 UEBA use case above), wherein attackers using stolen credentials hack the corporate network. If an account logs in at odd hours or attempts to escalate privileges, chances are non-zero that an attack is in process. Properly manned UEBA systems can immediately flag these compromised activities and notify the right teams to curtail the activity.
Differences Between UEBA and UBA Security
User and entity behavior analytics (UEBA) and user behavior analytics (UBA) are security technologies focused on identifying potentially harmful network activity. They have subtle differences mainly related to data sources and inadequate actor identification.
UBA analyzes users – specifically, their actions relative to network interaction – to spot malicious behavior. The reliance on user log data hampers UBA systems from looking outside the human user – which is where the “entity” part of UEBA comes into play.
UEBA analyzes human and machine actors, or ‘entities,’ such as devices, applications, and networks. This scope allows for the sifting of log files, packets, and other application-related data sets, providing a more comprehensive view of network activity.
Use Cases
UEBA covers a wide array of use cases that extend beyond individual user actions: UEBA systems can detect anomalies related to devices, such as the appearance of unregistered network devices, which could indicate a compromised device.
This broad detection capability makes UEBA highly effective in environments with complex infrastructures and diverse (on-prem, cloud, hybrid) IT assets. It provides comprehensive protection against insider threats, data exfiltration, ransomware, and brute-force attacks.
User behavior analytics, as specified above, only targets the actions of individuals within an organization. UBA thus can excel in detecting insider threats, where user activities like login times are a familiar baselined activity to scan for abnormalities. This more concentrated approach is precious in industries such as banking and healthcare, where protecting customer information is highly regulated.
Data Sources
Most organizations have a diverse range of data sources. These sources include traditional user activity logs, such as email interactions and network access logs, and data from network devices like endpoints, servers, and network equipment. Additional inputs might come from security solutions like intrusion detection systems (IDS), antivirus tools, and external threat intelligence feeds.
Integrating varied data sources allows UEBA systems to construct comprehensive behavioral profiles for both users and entities, enabling the detection of complex threats involving multiple components of an IT environment, such as advanced persistent threats.
On the other hand, UBA focuses predominantly on data about human interactions with internal IT systems. Primary data sources for UBA include system logins, file access, database queries, and application monitoring: user-centric logs are pivotal for establishing baseline behaviors. This narrower scope of data sources is sufficient for organizations primarily concerned with user behavior—human-based security breaches.
Analytics
UEBA employs advanced analytics techniques, encompassing machine learning, statistical analysis, and data science methodologies, to evaluate and correlate activities across various data points.
This approach enables pattern recognition across user and entity behaviors—anything from anomalous activity to false positives. The analytics are designed to dynamically adapt and update behavioral baselines as patterns evolve, making UEBA an agile and highly effective tool in the continuously shifting landscape of cybersecurity threats.
In contrast, user behavior analytics primarily analyzes user-specific data, such as login times, resource access, and usage patterns. UBA systems apply similar machine learning and statistical techniques as UEBA but concentrate these tools on individual user actions to detect suspicious behavior that could indicate potential security breaches, like data theft or misuse of privileges.
This focused approach allows UBA to excel in environments where user actions are the primary concern, providing detailed tracking of normal behaviors to pinpoint potential insider threats quickly. While UBA is highly effective in detecting human-related security risks, it lacks coverage of the broader entity activities that UEBA provides, making it less equipped to handle threats involving non-human entities within the network.
UEBA Analytics Methods
UEBA communicates system (machine) and human user activity irregularities, harnessing the power of machine learning models to analyze expansive data sets. The UEBA analytics method includes:
- Creating baseline data points for everyday activities and schedules at employee, team, department, and company levels.
- Alerting cyber admin teams if a user or entity shows signs of a breach or attack so that human assessment and response begin immediately.
- Detecting compromised account credentials by continuously monitoring anomalous network login activity while looking for unrecognized login locations and times.
- Detect if and which endpoints exhibit abnormal activity, regardless of on-premise or remote access control.
UEBA Best Practices
For companies adopting UEBA security solutions for the first time or refining processes to make the software more impactful for their admins and broader teams, here are four best practices that should inform:
1. Account for Insider and External Threat Patterns when Creating Policies and Rules
Companies today must account for many potential network issues, including external threats. The design phase of any security effort – particularly one with UEBA in mind – must consider ALL threat types to create compelling user and access policies.
2. Triage Alerts to Relevant Management or Cybersecurity Team Members
UEBA solutions produce data insights – those insights require precise, human-led responses. Therefore, organizations must triage alerts to those who can effectively troubleshoot, solve, and report.
3. Include Privileged and Unprivileged Users in the UEBA Schema
Unfortunately, anyone can be an insider threat, even unintentionally. UEBA is a protective layer designed for an entire organization, agnostic of position, experience, or access. Apply it to all stakeholders equally to maximize UEBA benefits.
4. Couple UEBA Software with Other Cybersecurity Solutions to Achieve 360-Degree Protection
UEBA is not a standalone security solution or approach. UEBA software plays more like a point guard on a basketball team whose ultimate responsibility is to protect the ball and ensure it gets into the right hands. It is undoubtedly an important part, but still just a part of a holistic security approach.
FAQs
What is the difference between SIEM and UEBA?
SIEM and UEBA are both cybersecurity solutions, but they are different. SIEM collects and analyzes log data from various sources to identify security events. In contrast, UEBA focuses on detecting abnormal user and entity behavior to identify potential insider threats.
What is the difference between UEBA and EDR?
UEBA focuses on detecting abnormal behavior of users and entities to identify potential insider threats. EDR is a cybersecurity solution that monitors explicitly and responds to security incidents on individual endpoints.
What is the difference between IAM and UEBA?
IAM focuses on managing user identities, controlling resource access, and ensuring authentication and authorization. UEBA focuses on detecting anomalous behavior and potential insider threats by analyzing patterns and deviations from normal user behavior within a network environment. While IAM and UEBA serve different purposes, they can complement each other to enhance overall cybersecurity measures.
Is UEBA part of EDR?
No, UEBA is not part of EDR. EDR is a cybersecurity solution that monitors explicitly and responds to security incidents on individual endpoints. At the same time, UEBA focuses on detecting anomalous behavior and potential insider threats within a network environment.
How does UEBA work?
UEBA monitors and analyzes user behavior patterns within a network environment to identify potential insider threats. It uses advanced algorithms and machine learning techniques to detect anomalies and deviations from normal user behavior, providing real-time alerts and insights for proactive security measures.
What are the three pillars of UEBA?
The three pillars of UEBA are data collection, behavior profiling, and analytics. Data collection involves gathering information from various sources, such as logs, endpoints, and network traffic. Behavior profiling analyzes user behavior patterns to establish baselines and detect anomalies. Analytics uses machine learning algorithms to identify potential insider threats and provide real-time alerts for proactive security measures.
Does UEBA use AI?
UEBA utilizes AI technologies such as machine learning and advanced algorithms to analyze user behavior patterns and detect anomalies, enabling real-time alerts and proactive security measures.
What are the key benefits of UEBA?
UEBA offers several key benefits, including detecting insider threats, identifying anomalies in user behavior, and providing real-time alerts for proactive security measures. By leveraging AI technologies such as machine learning, UE enhances overall cybersecurity measures by monitoring and analyzing user behavior patterns within a network environment.
What are the capabilities of UEBA?
UEBA has capabilities such as detecting insider threats, identifying anomalies in user behavior, and providing real-time alerts for proactive security measures within a network environment. It leverages AI technologies like machine learning to analyze user behavior patterns and enhance overall cybersecurity measures.
Is UEBA machine learning?
No, UEBA is not machine learning itself. However, UEBA utilizes machine learning algorithms and advanced analytics techniques to analyze user behavior patterns and detect anomalies within a network environment.
Conclusion
UEBA security tools protect networks through both human and non-human interactions. Implementing UEBA means engaging the design relative to existing security systems and focusing on the proper use cases. This includes integrating the UEBA tool with other security stack software, like SIEM and IAM solutions, to broaden security system integration and benefits.
While UEBA exemplifies an adaptive security approach, it’s still just one piece of a more extensive security system. To get the most value out of your UEBA stack, you still need human input, including the right stakeholder alerts and an experienced security team.
