How should security teams model lateral movement in complex environments?

Why This Matters for Security Teams

lateral movement is where many well-designed security programs lose realism. A vulnerability list can show exposure, but it does not show what an attacker can actually reach after one foothold is obtained. Modelling reachability as a graph helps teams see how identity, network trust, remote administration, and workload permissions combine into routes across environments. That matters in hybrid estates where an endpoint, a cloud workload, and a SaaS tenant may all be connected by hidden trust relationships.

For security leaders, the practical risk is not the first compromise alone. It is the ability to turn one compromised account, token, or service into access elsewhere. That is why threat modelling should be tied to attacker behaviours such as credential abuse, remote service use, and privilege escalation. A useful reference point is the MITRE ATT&CK Enterprise Matrix, which helps teams map common movement techniques to actual environment paths.

In practice, many security teams encounter lateral movement only after an incident review reveals that “low-risk” access was enough to cross multiple trust boundaries, rather than through intentional path analysis.

How It Works in Practice

Effective modelling starts by defining nodes and edges with enough detail to reflect real attacker options. Nodes are not just hosts. They also include identities, groups, service accounts, API gateways, directories, clusters, and third-party trust points. Edges represent the ways movement can occur: remote desktop, SSH, WinRM, database links, service-to-service credentials, federation trust, delegated admin rights, or access via stolen secrets. Current guidance suggests that graph accuracy improves when identity paths are modelled alongside network connectivity, because many enterprise breaches pivot through permissions rather than open ports alone.

A practical workflow usually includes:

• Inventorying reachable assets, privileged identities, and trust relationships across on-premises and cloud environments.
• Adding edge types for authentication, delegation, token reuse, shared credentials, and administrative tooling.
• Weighting edges by attacker effort, detection likelihood, and business impact.
• Testing the graph against known techniques from the MITRE ATT&CK Enterprise Matrix and validating whether the path is actually executable.
• Using the results to prioritise segmentation, credential hardening, JIT access, and PAM coverage where they reduce the most high-value paths.

The goal is not to model every theoretical route. It is to identify the shortest and most credible paths to crown jewels, especially where an attacker can reuse identity, bypass MFA through token theft, or exploit trust between automation systems and production workloads. Good graphs also distinguish between technically possible and operationally likely paths, because noisy or brittle routes may not matter in a real intrusion.

These controls tend to break down in highly dynamic environments where ephemeral workloads, shared automation identities, and poorly documented federation trusts change faster than the graph can be updated.

Common Variations and Edge Cases

Tighter modelling often increases operational overhead, requiring organisations to balance path fidelity against the cost of continuous data collection and graph maintenance. That tradeoff is especially visible in large cloud estates, OT-adjacent networks, and developer platforms where permissions change frequently. Best practice is evolving here: there is no universal standard for how much path detail is enough, so teams should calibrate to risk rather than chase perfect completeness.

Edge cases matter. In zero trust environments, lateral movement may be constrained by policy, yet stolen identity can still unlock authorised access if device posture, session controls, or token lifecycle are weak. In SaaS-heavy environments, movement may not look like network hopping at all; it can occur through delegated app consent, mailbox rules, admin APIs, or mis-scoped service principals. In containerised and Kubernetes-based systems, movement may follow secrets mounted into pods, workload identity bindings, or overly broad cluster roles. In such cases, the graph must include identity and secret flow, not just subnet adjacency.

Teams should also separate normal administrative activity from attacker-like movement. That requires telemetry from IAM, EDR, SIEM, and cloud control planes, plus a clear view of which edges are legitimate by design. If that separation is weak, the graph becomes too noisy to support decisions. For deeper path-based technique mapping, the ATT&CK matrix remains a useful reference, but it should be paired with local architecture knowledge and trust assumptions.

Related resources from NHI Mgmt Group

• How should security teams limit identity-driven lateral movement in hybrid environments?
• How should security teams reduce lateral movement risk in AI environments?
• How should security teams run tabletop exercises for lateral movement prevention in IoT and OT environments?
• How should security teams detect lateral movement across SaaS applications?