Your MDR services are falling short. How can you use user and entity behavior analytics to improve them?

Consider a financial institution employing UEBA to enhance its MDR service. UEBA detects an anomalous pattern of activity involving a key employee accessing unusual websites and transferring large amounts of data. UEBA's alert triggers an immediate investigation, leading to the discovery of an insider threat attempting to exfiltrate sensitive data. By integrating UEBA into MDR services, organizations can gain a deeper understanding of user and entity behavior, leading to more effective threat detection, reduced false positives, and automated responses. This empowers security teams to proactively identify and mitigate threats, safeguarding their sensitive data and maintaining business continuity.

UEBA is a monitoring technique that focuses on observing the behavior of end users. UEBA tools create user profiles by analyzing typical behavior patterns. Using the gathered data, the UEBA system establishes a baseline of normal behavior for each user. Anomaly detection is then employed to monitor for deviations from this baseline, which may suggest malicious activity.

The UEBA aims to detect anomalies in the behavior of users and entities that may signal cyberattacks or malicious intentions. To do this, it uses machine learning to analyze data from various sources, such as network equipment, security tools, authentication databases, threat intelligence feeds, ERP or HR systems, among others. The UEBA also benefits from the integration with other products and systems of security already existing in the organization, increasing its robustness and effectiveness. In addition, the UEBA contributes to speed up the detection and response to incidents, reducing the time and cost of mitigating threats.

Of course, MDR needs UEBA data and visibility. This is the whole point of MDR and, in my view, MXDR. A customer expects you not only to give them a great report but also to cover every attack surface. UEBA is only part of that. You also need analytics for the cloud, on-premises, SaaS, your data centers, remote offices, users, and endpoints. So I agree, UEBA is a key part of MDR, but it’s only one aspect of having a leading MDR service. We see XDR (and hence MXDR) adding visibility to the entire attack surface, and it’s the key reason I just joined Stellar Cyber. Our Open XDR ensures you can say yes to any current data needed either through our sensors or our partner ecosystem.

Para aprimorar os servi?os de Detec??o e Resposta Gerenciada (MDR), a incorpora??o da análise de comportamento de usuários e entidades é crucial. Ao monitorar padr?es de atividade, acessos e intera??es, é possível identificar comportamentos an?malos que podem indicar amea?as. Implementar solu??es de User and Entity Behavior Analytics (UEBA) permite uma detec??o mais precisa de atividades suspeitas, melhorando a capacidade de resposta a incidentes. Além disso, a análise comportamental contribui para ajustes contínuos nas políticas de seguran?a, refor?ando a postura de defesa contra amea?as cibernéticas em constante evolu??o.

MDR/SIEM tends to start with detection of know evil. 90+% focus on malware. Known evil IP, URL, or Hash. This will fail to detect the zero day and require endless signature and rule updates. UEBA focuses on the "weird" - What has the user/machine done today that is different? higher count, rare events, unusual compared to similar users/systems. These often find DLP issues, insider threats, compromised accounts...these are the bulk of the actual 0 Day, and large breaches as reported at the DFIR conferences and in most breach reports. These aren't just a small improvement, they are a huge leap forward. However implementation is seldom done well. It takes new log sources - IAM and Applications are key to new business relevant use cases.

UEBA improves MDR services by detecting unusual behavior, reducing false alarms, spotting insider threats, understanding normal behavior patterns, aiding incident response, and providing continuous monitoring.

User and Entity Behavior Analytics (UEBA) is a powerful tool that can greatly enhance Managed Detection and Response (MDR) services. UEBA leverages advanced analytics and machine learning algorithms to detect and respond to anomalous user and entity behavior within an organization's network. By analyzing patterns and trends in user behavior, UEBA can identify potential security threats and help MDR services proactively mitigate risks.

Integrating User and Entity Behavior Analytics (UEBA) into our Managed Detection and Response (MDR) services enhances threat detection and incident response. UEBA analyzes user behavior to identify abnormal patterns, creates baselines for normal behavior, and monitors entity interactions to detect suspicious activity. Machine learning improves threat detection, while behavioral context enriches alerts for faster investigation. UEBA identifies high-risk entities for focused monitoring and continuously updates behavior models to adapt to changing threats, strengthening our security approach.

A retail company employing UEBA within its MDR service discovers an anomalous pattern of activity involving a salesperson downloading confidential customer data and transferring it to an external cloud storage service. UEBA's alert triggers an immediate investigation, leading to the identification of an employee attempting to sell sensitive customer information on the dark web. By integrating UEBA into MDR services, organizations can gain a deeper understanding of user and entity behavior, enabling them to proactively detect, respond to, and prevent cyberattacks. This empowers security teams to safeguard their sensitive data, maintain business continuity, and gain a competitive edge in the digital landscape.

A healthcare organization integrates UEBA into their MDR service to detect anomalies in user behavior. The UEBA solution identifies a pattern of activity involving a physician accessing sensitive patient records during unusual hours and transferring them to an external storage location. UEBA's alert triggers an immediate investigation, leading to the discovery of an insider threat attempting to sell patient data on the dark web. By following these structured steps and continuously evaluating the effectiveness of UEBA, organizations can effectively harness its capabilities to enhance their MDR services, enabling proactive threat detection, reduced false positives, and accelerated incident response.

The deployment UEBA is to improve security indicators, such as: the number and quality of alerts generated, the time and cost of detection and response to incidents, the reduction of false positives and irrelevant alerts, the feedback and recommendations for security improvement. The integration of UEBA with SIEM provides a more complete, accurate and agile view of the behavior of users and entities, enabling a more effective detection and response to cyber threats.

As an incident responder, looking into multiple infrastructures that UEBA can be of great help to teams that are trying to automate detection. I would recommend before implementing this great tech to perform a Compromise Assessment that includes all infrastructure endpoints in its scope and users, and heavily monitoring the endpoints during the learning period of the UEBA. This will provide a clean slate for a baseline and eliminate the possibility of a AI learning that a threat actor already inside the network is a normal behavior.

Org. face ever-evolving cyber threats that can have severe consequences if not promptly detected and mitigated. MDR services have emerged as a crucial component in safeguarding organizations' digital assets. To enhance the effectiveness of MDR services, implementing User and Entity Behavior Analytics (UEBA) can provide an added layer of security. UEBA uses advanced analytics to detect abnormal user and behavior, helping organizations identify potential threats before they cause harm. The implementation of UEBA for your MDR services requires careful planning and execution. please follow the below steps. Defining Use Cases and Objectives Selecting the Right UEBA Solution Integrating UEBA with MDR Services Training Your Security Team on UEBA

This is very important. At the same time that user behavior type of monitoring rule can be very helpful, it can introduce serious flaws in your detective controls. Consider that you have been breached already, where threat actors gained persistency and opened back doors on your environment, but sitting tight slowly moving laterally or sniffing the network waiting for a domain admin to send credentials in clear text, a couple of months before initiating exfiltration or encryption of your files. If you initiate learning during this scenario (which you will never know), this will be set as expected. Implement UEBA on already existing rules, based on very specific scenarios, and review events

Correlating users behaviour with other security events and indicators UBA supports in identifying potential security threats for example : insider threats, compromised accounts and unauthorized access attempts

A financial institution, leveraging UEBA within its MDR service, identifies an unusual pattern of activity involving a senior executive accessing sensitive financial data from an unauthorized location. UEBA's alert triggers a swift investigation, revealing an attempted data breach by an external entity. By leveraging UEBA's capabilities, the financial institution safeguards its confidential data and protects its reputation. By embracing UEBA, MDR services can elevate their security posture, enhance operational efficiency, and deliver exceptional customer experiences. This, in turn, fosters trust, drives growth, and strengthens the organization's competitive edge in the ever-evolving cybersecurity landscape.

Benefits to UEBA in MDR include, reduction of false positives, accelerated IR, identifying APTs based on anomalies of specific signatures or IOCs UEBA should provide enhanced threat visibility, more proactive response, and better protection for customers against sophisticated and evasive attacks that often bypass traditional protective measures.

Implementing UEBA for your MDR services brings numerous benefits that enhance your overall security capabilities. By leveraging UEBA's advanced analytics, you can improve threat detection and response, ensuring timely mitigation of potential security incidents. The enhanced security posture and proactive approach to threat detection contribute to increased customer satisfaction and loyalty. Furthermore, the integration of UEBA differentiates your MDR services, giving you a competitive advantage in the market. Embrace the power of UEBA and unlock the full potential of your MDR services.

UEBA solutions often provide features that help organizations meet regulatory compliance requirements by monitoring and analyzing user activities and access controls. It can provide valuable context and insights into security incidents, facilitating faster and more accurate incident response actions by MDR teams. By analyzing user and entity behavior, UEBA can help prioritize security incidents based on risk, allowing MDR services to focus on the most critical threats first.

Key Challenges of UEBA for MDR can be Data Quality and Availability, Privacy and Compliance Concerns, Alert Fatigue and False Positives, Limited Threat Coverage and Cost and Complexity.

La “E” de “UEBA” significa “entidad” y viene de una Guía de Mercado de Gartner publicada en 2017. Esa fue la primera vez que se utilizó el término “UEBA” en lugar de “UBA”. Hasta entonces, el objetivo principal de la tecnología UBA era detectar el robo de datos y el fraude. Pronto se dieron cuenta de que las ciberamenazas llegan a otros sitios que no eran los usuarios. Entre los beneficios UEBA, combinada con un sistema SOAR, es una forma muy eficaz de identificar y prevenir de manera proactiva los ciberataques y de reducir la exposición de una organización a las ciberamenazas.

UEBA assigns risk scores to users and entities based on their behavior. Hence, Don't forget the basics. Defining risk scoring that fits your enterprise risk assessment model and reduce false positives to ensure you focus on right areas.

One of the primary challenges of UEBA is ensuring the quality and availability of data. UEBA relies on comprehensive and accurate data from various sources to generate meaningful insights. Incomplete or inconsistent data can hinder the accuracy and effectiveness of UEBA analytics. UEBA systems generate alerts based on detected anomalies in user and entity behavior. However, the challenge lies in managing the volume of alerts and distinguishing between true threats and false positives. An excessive number of alerts can lead to alert fatigue, overwhelming security teams and diminishing the efficiency of MDR services. It requires finetuning the UEBA system and implementing effective process to prioritize and respond to alerts appropriately.

Implementing UEBA effectively and integrating it with existing incident response processes and remediation workflows can be complex and will require a skilled analyst who can configure, monitor and interpret the outputs of the platform. MDR teams will need to establish a seamless workflow where UEBA alerts are prioritised, investigated promptly, and responded to effectively to minimise the impact of potential threats.

UEBA or simply analyzing entity behaviors is one of the best ways to detect possible suspicious activities. However one thing always should consider is UEBA detections will mostly roam around the mid levels of attack map. This means attackers have already passed one or more layers to be here. UEBA should be used in alignment with security controls in other layers for maximum effectiveness. Otherwise, it's just another tool you will have in your detection suite with no use.

UEBA (User and entity behavior analytics) is a powerful solution to minimize efforts on designing use cases, but you need to be careful when using those. Your systems might be compromised with a silent APT, when you start the UEBA learning period, malicious activities might be considered authorized and you may suffer false-negatives (rules not triggering for true incidents). I'd recommend using UEBA for very specific use cases to support your current set of threat detection rules, but not as your main source of rules

UEBA uses a combination of behavior-based rules and machine learning algorithms to establish baselines and detect deviations from normal patterns. While there are other Enterprise Immune Systems (EIS) such as Daktrace that uses a self-learning and adaptive approach that does not rely on predefined rules or signatures to identify threats. EIS has various products and modules for different security domains, such as network, cloud, email, and IoT. EIS and UEBA are different cybersecurity solutions that have some similarities and differences in their scope and approach. They can be used together or separately to enhance the security posture of an organization.

UEBA solutions that lean too heavily on behavioral analysis that sits on the endpoint tend to miss malicious activity stemming from other areas such as Active Directory, cloud workflows, and network traffic. Effective UEBA detection algorithms must be fed security-relevant data from a broader dataset to be truly effective.

UEBA overs both the behavior of users and entities within the network including network and Database. But to utilize the full potential of this solution it is important to align with specific needs and requirements of an organization, including the ability to define custom rules and alert threshold, and how to integrate this to other solutions such as firewall and IPS.