Lateral Movement Detection in Splunk

Lateral movement is one of the most critical phases of an attack lifecycle and a key focus area for SOC teams and threat hunters. Once attackers gain an initial foothold, their next objective is to move laterally across systems to expand access, escalate privileges, and reach high-value assets. Detecting this activity early can significantly limit impact and prevent full compromise.

Splunk plays a central role in lateral movement detection by correlating authentication, endpoint, and network logs across the environment. This blog explains what lateral movement looks like in practice, how attackers behave, which log sources matter most, and how Splunk can be used effectively for detection and threat hunting.

What Is Lateral Movement in Cyber Attacks

Lateral movement refers to the techniques attackers use to move from one compromised system to other systems within the same environment. Unlike initial access, lateral movement often uses legitimate credentials, trusted protocols, and native administrative tools, making it harder to detect.

Common attacker goals during lateral movement include:

• Discovering additional systems and users
• Reusing credentials across hosts
• Escalating privileges
• Accessing sensitive servers or data

From a detection standpoint, lateral movement usually appears as abnormal internal authentication or network activity rather than external attack traffic.

Why Lateral Movement Detection Is Critical for SOC Teams

Most large breaches do not stop at a single compromised endpoint. Attackers often spend time moving laterally to establish persistence and identify valuable targets. If lateral movement goes undetected, containment becomes far more difficult.

Effective lateral movement detection allows SOC teams to:

• Identify compromised credentials early
• Detect internal reconnaissance and abuse
• Interrupt attacker progression
• Reduce dwell time and blast radius

Splunk’s ability to correlate data across multiple log sources makes it well-suited for this use case.

Attacker Behavior During Lateral Movement

Understanding attacker behavior helps shape detection logic. Lateral movement is not random; it follows patterns based on environment design and available access.

Typical behaviors include:

• Repeated authentication attempts across multiple hosts
• Use of administrative protocols between internal systems
• Access to systems the user has never interacted with before
• Rapid movement between hosts in short time windows

These behaviors often blend into normal activity unless analyzed at scale.

Key Log Sources for Lateral Movement Detection

Lateral movement detection depends on visibility into internal activity. Relying on a single log source is rarely sufficient.

Authentication Logs

Authentication logs are foundational. They show who accessed what, from where, and when.

Useful signals include:

• Same user authenticating to multiple hosts
• Logins between peer systems rather than user workstations
• Use of administrative or service accounts across hosts

Authentication logs often provide the first indication of lateral spread.

Endpoint Logs

Endpoint logs capture process execution and local activity on systems.

Indicators include:

• Execution of remote management tools
• Credential usage on multiple endpoints
• Access to administrative shares or services
  

These logs help confirm whether lateral movement involved direct system interaction.

Network Logs

Network logs reveal how systems communicate internally.

Key indicators include:

• Unusual east-west traffic patterns
• Connections between systems that do not normally communicate
• Use of management or file-sharing protocols

Network visibility is especially important when attackers avoid generating authentication events.

Common Lateral Movement Techniques Visible in Logs

Attackers use a mix of techniques depending on access and environment maturity.

Credential Reuse Across Hosts

One of the most common lateral movement techniques is reusing valid credentials to access multiple systems.

Log indicators include:

• Same user authenticating to many hosts
• New host access without prior history
• Short time gaps between logins

This pattern is often detectable through aggregation and baselining.

Administrative Protocol Abuse

Attackers frequently rely on built-in administrative protocols to move laterally.

From a detection perspective, this appears as:

• Increased internal management connections
• Use of privileged accounts across multiple systems
• Activity originating from non-administrative endpoints

This behavior often blends into normal admin traffic if not analyzed contextually.

Peer-to-Peer Host Access

Workstations typically do not authenticate to each other. When they do, it may indicate lateral movement.

Indicators include:

• Workstation-to-workstation authentication
• File or service access between peer systems
• Activity outside standard workflows

This pattern is particularly valuable for detecting early-stage lateral movement.

Detecting Lateral Movement with Splunk

Splunk enables lateral movement detection by aggregating and correlating events across users, hosts, and time.

Aggregation Across Hosts

A common detection approach involves aggregating authentication or access events by user and counting distinct destination systems.

Conceptually, this highlights:

• Users accessing an unusually high number of hosts
• Sudden spikes in internal access activity

This approach is effective when paired with historical baselines.

First-Time or Rare Access Detection

Lateral movement often involves access to systems a user has never touched before.

Detection logic focuses on:

• First-seen user-to-host relationships
• Rare access patterns compared to historical behavior

Splunk can track these relationships over time and flag anomalies.

Time-Based Correlation

Attackers often move quickly once access is gained.

Time-based detection looks for:

• Multiple host accesses within short windows
• Rapid switching between systems
• Burst patterns inconsistent with normal workflows

This helps separate routine administrative work from suspicious behavior.

Threat Hunting for Lateral Movement in Splunk

Not all lateral movement will trigger alerts. Threat hunting uses exploratory analysis to identify subtle patterns.

Effective hunting strategies include:

• Reviewing users with expanding host access over time
• Identifying systems acting as unexpected access hubs
• Searching for internal activity following external compromise alerts

Splunk’s flexible search capabilities make it well-suited for iterative hunting workflows.

Reducing False Positives in Lateral Movement Detection

Lateral movement detection can generate noise if context is not applied.

Common false positive sources include:

• Legitimate administrative activity
• Automated system processes
• Patch management and monitoring tools

To reduce noise:

• Exclude known management systems
• Separate user and service account behavior
• Apply role-based or system-based baselines

Good tuning ensures analysts focus on meaningful signals.

Operational Considerations for SOC Teams

Detection logic alone is not enough. SOC teams need clear processes around lateral movement alerts.

Operational best practices include:

• Clear alert descriptions explaining why activity is suspicious
• Defined investigation steps for analysts
• Correlation with identity, endpoint, and network context
• Escalation paths for confirmed compromise

Lateral movement alerts should drive investigation, not confusion.

Best Practices for Lateral Movement Detection

Organizations can strengthen detection by following these practices:

• Ensure comprehensive authentication and internal network logging
• Normalize user, host, and protocol fields
• Build historical baselines for internal access patterns
• Combine multiple weak signals into stronger detections
• Continuously tune logic based on incident outcomes

Lateral movement detection improves over time as understanding of the environment deepens.

Conclusion

Lateral movement detection is a critical capability for effective threat detection and response. Attackers rely on internal movement to expand access and achieve their objectives, often using legitimate credentials and trusted protocols. Splunk enables SOC teams to detect this behavior by correlating authentication, endpoint, and network logs across the environment. By understanding attacker behavior, focusing on abnormal internal access patterns, and continuously tuning detection logic, organizations can significantly reduce attacker dwell time and limit the impact of compromise.