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12/07/2026 12:14 pm
TL;DR: As organisations spread workloads across AWS, Azure and Google Cloud Platform, manual segmentation and fragmented visibility create blind spots that make lateral movement harder to detect, according to Illumio. The practical issue is not cloud choice but whether policy, telemetry and enforcement can stay consistent across environments.
NHIMG editorial — based on content published by Illumio: Illumio strengthens Google Cloud Platform support to contain risk across distributed environments
Questions worth separating out
Q: How should security teams validate cloud segmentation in practice?
A: Security teams should validate segmentation by testing observed traffic against the intended policy boundary, not by relying on configuration alone.
Q: Why do service accounts and workloads still create lateral movement risk in cloud environments?
A: Because many environments still trust the host, namespace, or cloud boundary more than the workload's actual state.
Q: What breaks when cloud segmentation is managed separately in each platform?
A: What breaks is policy consistency.
Practitioner guidance
- Centralise segmentation policy intent Define one policy model for workload communication across AWS, Azure and GCP, then map cloud-native controls back to that model so exceptions are visible rather than implicit.
- Validate east-west traffic paths continuously Use visibility data to review which workloads are talking to each other, then remove or constrain paths that are not tied to explicit business dependencies.
- Treat workload identities as containment boundaries Tie service-to-service access to explicit workload identity and application dependency maps so access can be restricted when the trust relationship is not required.
What's in the full article
Illumio's full blog covers the operational detail this post intentionally leaves for the source:
- How Illumio applies segmentation policy across AWS, Azure and Google Cloud Platform in practice.
- What the agentless integration changes for deployment effort and operational rollout.
- How Illumio Insights maps distributed workload behaviour and blind spots inside GCP.
- Why the vendor frames containment as a response to lateral movement in multi-cloud environments.
👉 Read Illumio's analysis of GCP support for multi-cloud segmentation and containment →
Google Cloud segmentation and visibility gaps in multi-cloud?
Explore further
12/07/2026 12:28 pm
Distributed cloud security is becoming a policy consistency problem, not just a tooling problem. The article reflects a broader reality in which security teams are expected to maintain the same control intent across AWS, Azure and GCP while each platform behaves differently. That makes segmentation governance harder than simple deployment, because drift is introduced whenever policy must be translated by hand. Practitioners should treat multi-cloud consistency as an operating model issue, not a feature checklist.
A question worth separating out:
Q: How do teams know if microsegmentation is actually working?
A: Microsegmentation is working when a compromised workload cannot reach anything outside its explicit policy boundary. The best signal is not the existence of a segmentation design, but the reduction in reachable assets after compromise. If east-west traffic still flows broadly, the control is not changing attacker economics.
👉 Read our full editorial: Multi-cloud segmentation in GCP: why visibility gaps persist
