How to Investigate Lateral Movement Using Flow Data

Lateral movement is rarely loud. Once an attacker gains an initial foothold, the next phase often blends into normal network activity, using legitimate credentials, familiar protocols, and trusted paths. That subtlety is what makes lateral movement difficult to investigate with tools that focus only on endpoints or individual alerts.

Flow analytics offers a different vantage point by treating the network itself as evidence. Instead of asking whether a single event looks malicious, investigators can ask how behavior changes over time, across peers, and between services.

This blog will outline a practical way to investigate suspected lateral movement using flow data. The goal is not instant attribution, but clarity about what changed, where it spread, and whether activity aligns with expected behavior patterns.

Step 1: Establish peer awareness, not just asset lists

Lateral movement depends on discovery. Compromised systems need to learn what other hosts exist and which ones are worth contacting.

From a flow perspective, this often appears as a change in peer relationships rather than a single suspicious packet. Hosts that previously communicated with a small, stable set of peers may suddenly begin touching many new internal systems, even if each connection is brief.

Instead of starting with known-bad indicators, investigators should first understand normal peer behavior. Flow analytics supports this by showing who typically talks to whom and how consistently.

Once that baseline is understood, deviations become easier to spot. Peer expansion does not automatically mean compromise, but it does create a short list of systems worth deeper scrutiny, especially when discovery-like behavior precedes other anomalies.

Step 2: Look for unexpected service access

After discovery comes access. Lateral movement frequently uses administrative or management services that already exist in the environment. Because these services are legitimate, they rarely trigger signature-based detections on their own. Flow data helps by highlighting context rather than content, such as which services a host accesses and how that differs from its usual role.

Unexpected service access often shows up when a system begins using protocols it has no historical reason to use, or when access patterns shift outside normal time windows or peer groups.

For example, a workstation initiating administrative sessions to multiple servers may not violate a rule, but it does violate expectations. Investigators should treat these shifts as prompts for correlation, not conclusions, and examine whether access aligns with change windows, maintenance activity, or known workflows.

Step 3: Interpret traffic spikes as signals, not proof

Volume changes matter during lateral movement, but not all spikes indicate malicious behavior. Attackers may transfer tools, enumerate file systems, or move laterally in bursts that stand out against a host’s normal traffic profile. At the same time, legitimate activity such as backups or software updates can create similar patterns.

Flow analytics is useful here because it allows investigators to frame traffic spikes in context. Instead of asking whether volume increased, the more useful questions are where it increased, between which peers, and using which services. A modest spike across many internal peers can be more meaningful than a large spike to a single known destination.

Treating traffic volume as a directional clue rather than a verdict helps keep investigations grounded and claims-safe.

Step 4: Correlate anomalies into a behavioral narrative

Because no single indicator confirms lateral movement, correlation is key. When peer discovery, unexpected service access, and traffic anomalies occur in sequence or overlap on the same host, they begin to form a behavioral narrative. Flow analytics supports this by letting analysts view activity as a timeline of conversations rather than isolated events.

This narrative approach reduces reliance on assumptions. Instead of labeling behavior as malicious upfront, investigators can describe what happened in plain terms: which systems communicated, when new relationships appeared, and how usage patterns changed. That description is often sufficient to support next steps, whether that means containment, deeper endpoint inspection, or escalation to another team.

To summarize the investigative flow, effective lateral movement analysis typically follows this progression:

• Establish normal peer relationships, then identify expansion or drift
• Validate whether new service access aligns with expected roles or workflows
• Use traffic changes as context, not conclusions
• Correlate multiple weak signals into a single, time-ordered narrative

Why flow-based investigation holds up under scrutiny

One advantage of a flow-centric framework is defensibility. Flow records describe observed network behavior without inferring intent or inspecting payloads. This makes findings easier to explain across teams and easier to revisit later. When questions arise about why a host was isolated or an account was disabled, investigators can point to observable changes in communication patterns rather than opaque scores or alerts.

Flow analytics does not replace endpoint or identity data, but it provides a neutral backbone for investigation. By anchoring lateral movement analysis in peer behavior, service access, and traffic context, teams can move from suspicion to evidence methodically.

Looking to start using flow analytics to investigate lateral movement? Check out our flow-first observability platform, Plixer One.