Your SIEM is Lying to You: Why You Need UEBA to Uncover the Truth 

Introduction: The Evolution of Threat Detection  

The way we detect cyber threats has come a long way, but let’s be real—traditional methods have serious blind spots. Back in the day, we relied on correlation rules—basic if-this-then-that logic—to flag suspicious activity. It worked… sort of. But today, exponential data growth has limited the effectiveness of using only correlation rules to detect threats. The result? Analysts are overwhelmed with false positives, missed threats, and manual tweaking to keep up with evolving attack methods.  

According to the Ponemon Institute’s 2024 State of AI in Cybersecurity report, roughly 45% of those alerts are false positives. That’s nearly half of your SOC’s time wasted on noise. It’s no wonder correlation rules are struggling to remain relevant. This is where machine learning (ML) and artificial intelligence (AI) can help. These technologies, particularly user and entity behavior analytics (UEBA), are redefining what’s possible in threat detection. And who is leading the way? Exabeam, a five time recognized Leader in the Gartner Magic Quadrant for SIEM, with its groundbreaking ML-driven detection capabilities.  

The Early Days: When Correlation Rules Were Enough  

In the early years of SIEM, correlation rules were more than capable to stand on their own, they worked. These predefined logic statements—for example, “If five failed login attempts happen in ten minutes, trigger an alert”—helped organizations monitor their networks for suspicious activity. They navigated across log data, applied patterns, and highlighted known threats.  

But here’s the problem: correlation rules have limits. They can only identify threats based on what we already know (known/knowns). If it’s a new attack technique or a subtle twist to an existing technique, the rule won’t catch it. Worse, these rules are noisy, flooding a SOC with low-fidelity alerts. And because these rules don’t adapt on their own, analysts spend hours tweaking them just to keep up with changing threats. It’s an uphill battle, leaving security teams totally burnt out.  

The Shift to Machine-Learned Analytics: UEBA 

Machine learning helped flip the script. Unlike static correlation rules, UEBA doesn’t rely on pre-written logic. Instead, it uses ML algorithms to learn and baseline “normal” behavior. By analyzing user and entity actions over time, UEBA can pinpoint anomalies—whether it’s an insider behavior, a zero-day exploit, or a compromised identity.  

This is where UEBA shines. It doesn’t need to “know” the threat beforehand. It identifies behavior and applies risk based on the deviation, like an employee accessing sensitive data at 2 a.m. from an unfamiliar device. And because it focuses on patterns over time, UEBA cuts through the noise, reducing false positives and helping SOC analysts zero in on genuine threats.  

Why You Need Both: Correlation Rules and UEBA are Better Together  

UEBA doesn’t replace correlation rules—it makes them better. When used together, they deliver comprehensive security. Correlation rules are great for catching known, straightforward threats, like brute force attacks or unauthorized access attempts – the most common threats organizations face. They’re also useful for compliance and policy enforcement.  

UEBA fills in the gaps, it handles the gray areas where complex threats thrive. By combining the two, organizations can reduce the number of correlations they use, minimize rule tuning, and improve overall detection accuracy. UEBA’s behavioral insights even help refine correlation rules, making them more precise and effective.  

How UEBA Supercharges Correlation Rules  

Here’s how UEBA takes your existing SIEM to the next level:

• Fewer Rules, Better Results: With UEBA detecting behavioral anomalies, you can cut down on the number of correlation rules you need. This lightens the load on your system and the security engineers who are responsible for tuning.  
• Sharper Accuracy: UEBA provides context, helping you fine-tune correlation rules for fewer false positives.  
• Contextual Alerts: UEBA adds depth to alerts, showing whether an action is truly anomalous or just an outlier.  
• Smarter Incident Response: Correlation rules catch the initial event; UEBA provides the behavioral context, helping analysts act faster and smarter.  

Choosing the Right Vendor: Why Integration Matters  

Not all vendors get this balance right. Some bolt UEBA onto a traditional SIEM as an afterthought, leading to poor integration and a disjointed user experience. Exabeam stands out for its best-of-breed support for both correlation rules and advanced UEBA, delivering an integrated solution that maximizes threat detection, and boosts analyst productivity.  

Conclusion: The Future of Threat Detection is Within Your Reach  

Correlation rules alone can no keep up. Data volumes continue to explode, and the threat landscape is too complex. UEBA powered by machine learning is no longer “nice to have”—it is essential. By combining the best of both worlds—rules for the known, ML for the unknown—you get a defense system that’s proactive, adaptive, and ready for anything.  

Exabeam is leading the charge, equipping organizations with the tools they need to detect, investigate, and respond to critical threats. Whether your SIEM is on-premises or in the cloud, ML-driven UEBA from Exabeam helps SOCs worldwide uncover the truth their SIEMs might be missing.