Breaking Down the Real Meaning of an XDR Solution
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Learn how behavior-based analysis for real-time threat response empowers SOCs to detect
Lateral movement is no longer a secondary concern—it’s a core phase of modern cyberattacks. Once attackers breach an initial endpoint, they don’t strike immediately. Instead, they pivot silently across the network, escalate privileges, and hunt for sensitive assets. The longer they dwell, the more damage they’re capable of. That’s why detecting lateral movement with behavioral analysis is essential for modern cybersecurity defense.
This blog takes a deep dive into how behavior-based threat detection, especially when paired with Fidelis XDR and NDR, can uncover even the stealthiest signs of lateral movement before they escalate.
Lateral movement refers to the techniques adversaries use after an initial breach to navigate through the network, access multiple systems, and eventually reach high-value targets like domain controllers, databases, or cloud storage.
These lateral movement techniques are often low and slow—disguised as legitimate user actions, which makes traditional signature-based defenses fall short. Attackers exploit remote services, leverage stolen credentials, or inject malicious payloads across endpoints. That’s why lateral movement detection requires context—behavioral context.
| Aspect | Behavioral Analysis | Signature-Based Detection |
|---|---|---|
| Detection Method | Monitors patterns of behavior across users, endpoints, and networks | Matches activities to predefined rules or known malware signatures |
| Adaptability to New Threats | Learns and evolves with your environment to detect novel or stealthy attacks | Requires frequent updates; struggles with unknown or zero-day threats |
| Detection of Insider Threats | Identifies subtle behavioral deviations and compromised account activity | Often misses threats that resemble legitimate user behavior |
| False Positive Rate | Lower, due to context-aware anomaly detection | Higher, due to rigid rules and lack of behavioral context |
| Operational Efficiency | Enables proactive threat hunting and reduces alert fatigue | Reactive; can overwhelm SOCs with noise and irrelevant alerts |
| Best Use Cases | Advanced persistent threats, lateral movement, post-exploitation behavior detection | Traditional malware detection, known phishing signatures, rule-based risks |
Firewalls, antivirus tools, and even older SIEM solutions count on pre-set rules or known indicators. But attackers have changed the game. They fake user identities, copy admin actions, and hide within regular network activity.
Behavioral analysis offers another way. It tracks patterns, learns what’s normal, and spots anything unusual using user and entity behavior analytics (UEBA). Instead of looking at just the actions, it considers how those actions happen. This opens up new ways to detect behavioral anomalies and spot threats.
Older security tools were designed in a time when threats followed predictable patterns with clear indicators. These tools still serve a purpose but struggle to handle today’s flexible and hidden threats that often appear normal. This gap is why behavioral threat detection now plays an important role in cybersecurity.
Behavioral analysis focuses on knowing what is typical and then spotting anything unusual. Instead of just searching for malware patterns or risky IP addresses, it examines how users act how systems connect, and how information moves within the network. This kind of detection based on behavior provides a more flexible and effective way to identify advanced threats that traditional tools often miss.
Let’s break this into main parts:
Moving away from signature-based approaches allows for more flexibility. Whether identifying lateral movement in cloud environments, or internal threats detecting these risks becomes more effective.
Let’s break down the core of detecting lateral movement with behavioral analysis:
Behavior-based systems can flag common indicators of lateral movement, including:
When such behaviors align with post-exploitation behavior detection, they point toward network lateral movement in progress.
Using Extended Detection and Response (XDR), platforms like Fidelis Elevate correlate behavioral anomalies across endpoint, network, and cloud environments.
For instance:
Fidelis takes behavioral threat detection a step further by combining:
This integrated strategy allows organizations to monitor lateral network movement, even in hybrid and multi-cloud environments. Whether it’s cloud lateral movement detection or endpoint-level threat modeling, Fidelis connects all the dots.
Knowing how to stop lateral movement in a network involves more than spotting it. It demands architectural defense as well:
Wondering how does micro segmentation prevent lateral movement within a network?
When attackers make their move across your network, will you be ready?
With Fidelis NDR and Fidelis Elevate XDR, you’re equipped to:
Fidelis doesn’t just react—it anticipates. And in the world of lateral movement detection, that’s the edge your organization needs.
See Fidelis in action. Learn how our fast and scalable platforms provide full visibility, deep insights, and rapid response to help security teams across the World protect, detect, respond, and neutralize advanced cyber adversaries.