The digital age has started an era of connectivity, but with it comes a growing threat landscape. Companies of all sizes deal with a continuous flow of cyberattacks, ranging from advanced malware campaigns to focused insider threats. Traditional security measures often struggle to keep pace with the intuition of attackers. This is where Security Information and Event Management (SIEM) and User Entity and Behavior Analytics (UEBA) emerge as a powerful pairing.
SIEM acts as a central nervous system for security, collecting and analyzing data from various security tools to identify anomalies and potential security incidents. However, SIEM primarily focuses on “what” happened, not necessarily “who” or “why.” This is where UEBA steps in. UEBA delves deeper, analyzing user and entity behavior patterns to understand the “who” and “why” behind security events. By leveraging the combined power of SIEM and UEBA, organizations can gain a complete view of their security posture and proactively identify hidden threats before they can cause significant damage.
A Look at Insider Threats and Next-Level Attacks
While traditional security solutions can identify suspicious network activity or malware infections, they often struggle to detect more hidden threats like insider attacks. Here’s where the synergy between SIEM and UEBA shines:
- Enhanced Insider Threat Detection: Many insider threats involve seemingly valid user activity, making them difficult to detect with traditional methods. SIEM excels at identifying suspicious activities across the network, such as unauthorized access attempts or unusual data transfers. However, UEBA takes it a step further. By establishing user baselines for login attempts, file access, application usage, and other activities, UEBA can identify changes that might indicate malicious purposes. Imagine a user with access to sensitive data experiencing a sudden increase in file downloads from unfamiliar locations outside of their usual working hours. SIEM might flag the downloads as suspicious, but UEBA analysis can reveal if this is a one-time irregularity or a potential data theft attempt in progress. This combined insight empowers security teams to investigate and potentially prevent insider threats before they escalate into major breaches.
- Hunting Down Advanced Persistent Threats (APTs): Advanced Persistent Threats (APTs) are complex attacks where attackers gain access to a system, remain undetected for extended periods, and steal sensitive data. Traditional security solutions might struggle to identify these low-and-slow attacks that blend in with normal user activity. However, by comparing user behavior with security events, SIEM and UEBA can reveal slight irregularities that might indicate an APT in progress. For example, an attacker might compromise a valid user’s account and use it to access unauthorized resources. SIEM can identify the unusual access attempt, while UEBA analysis can reveal deviations from the user’s typical behavior patterns (e.g., accessing unusual resources, or logging in from unexpected locations). This combined intelligence allows security teams to detect and prevent APTs before they achieve their objectives.
Streamlining Investigations and Prioritizing Threats
Security teams are often overwhelmed with alerts from various security tools. This “alert fatigue” can lead to missed threats and wasted resources. The following describes how UEBA and SIEM collaborate to speed up investigations and rank threats:
- Improved Investigation Efficiency: Investigating security incidents can be a time-consuming process. SIEM and UEBA work together to streamline this process. SIEM correlates user activity with security, providing valuable context for investigation. UEBA adds another layer by analyzing user behavior patterns. Together, they present a clearer picture, allowing investigators to prioritize threats and focus on the most suspicious activities. For instance, a user logging in from an unusual location and accessing a critical server raises red flags. While SIEM can alert on the login, UEBA can reveal if this user has ever accessed that server before. This combined analysis helps determine the validity of the access attempt and speeds up the investigation by focusing on high-risk scenarios.
- Reduced Alert Fatigue: Security analysts are often overwhelmed with alerts from SIEM systems, leading to alert fatigue and potentially missed threats. UEBA helps solve this issue by analyzing user behavior context and prioritizing alerts. By identifying normal user baselines for login attempts, file access, and application usage, UEBA enables SIEM to concentrate on deviations that require further investigation. This reduces false positives and frees up valuable analyst time for critical security tasks. Consider a scenario where a user downloads a new file type. SIEM might flag it as suspicious, but UEBA analysis can reveal if this user frequently downloads similar files for their work, eliminating the need for unnecessary investigation. By prioritizing alerts based on user behavior context, UEBA empowers security analysts to focus on the most critical threats for a more efficient and effective security posture.
Machine Learning for SIEM and UEBA
The cyber threat landscape is constantly evolving, with attackers developing new techniques and tools to bypass traditional security measures. Here’s how machine learning-enabled SIEM and UEBA can keep businesses ahead of the curve:
- Continuous Threat Detection and Adaptation: Both SIEM and UEBA use machine learning to continuously learn and adapt. This allows them to detect new attack patterns and unusual user behavior that might escape traditional rule-based security tools. Machine learning also allows SIEM and UEBA to adapt to changing user behavior patterns over time. As user behavior evolves, the system can adjust its baselines to maintain accuracy in threat detection.
- Automated Threat Hunting and Incident Response: The integration of SIEM and UEBA with machine learning and automation platforms (SOAR – Security Orchestration, Automation, and Response) can further enhance security posture. Machine learning can identify potential threats and suspicious activities, while SOAR can automate tasks like isolating infected devices, blocking malicious IP addresses, and starting fixing processes.
Beyond Detection: Proactive Threat Prevention
While threat detection is crucial, the goal is to prevent attacks from happening in the first place. The following are some ways that UEBA and SIEM can support a proactive security strategy:
- User Behavior Anomaly Detection and Risk Scoring: By analyzing user behavior patterns, UEBA can identify users who perform unusual behavior that might indicate potential security risks. These users can then be targeted for additional security training or monitoring. Furthermore, UEBA can assign risk scores to user activities based on various factors (unusual location, time, access attempts to sensitive data). High-risk activities can trigger alerts and prompt further investigation, potentially preventing malicious actions before they occur.
- Security Baselining and Trend Analysis: SIEM and UEBA working together can establish security baselines for user activity, network traffic, and system health. By analyzing trends from these baselines, organizations can identify potential vulnerabilities or emerging threats before they rise into major security incidents. For example, a sudden spike in login attempts from a specific geographic location might indicate a potential credential-stuffing attack. Early detection allows organizations to take proactive measures like blocking suspicious IP addresses or implementing multi-factor authentication.
The Future of Security: A Collaborative Approach
The future of security lies in a collaborative approach that uses the strengths of different security tools and technologies. SIEM and UEBA, when combined and integrated with other security solutions, offer a powerful defense against evolving cyber threats.
The following guidelines should be kept in mind by companies looking to maximize the benefits of UEBA and SIEM:
- Data Integration and Normalization: SIEM and UEBA rely on data from various sources. Ensuring data is properly integrated is crucial for accurate analysis and threat detection.
- Security Expertise and Training: While SIEM and UEBA can automate many tasks, security analysts still play an important role in understanding data, investigating threats, and applying mitigation strategies. Investing in security expertise and training for your team is crucial.
- Continuous Monitoring and Improvement: Organizations need to continuously monitor their security posture, review SIEM and UEBA configurations, and adapt their strategies based on new threats and vulnerabilities.
Conclusion
By adopting a comprehensive security approach that uses the combined power of SIEM and UEBA, organizations can gain a deeper understanding of their security posture, identify hidden threats, and proactively prevent cyberattacks. This ultimately translates to a more secure and strong digital environment.