🧩 Microsegmentation

Least-Privilege Network Access for Apps, Users & Workloads

Microsegmentation prevents lateral movement by enforcing least-privilege network policyβ€”between users β†’ apps, workload β†’ workload, tier β†’ tier, and device β†’ serviceβ€”on premises, in cloud, and across Kubernetes.
SolveForce designs microsegmentation that’s identity-aware, posture-aware, and auditableβ€”integrated with ZTNA/SASE, NAC, EDR/XDR, and SIEM/SOARβ€”so you can contain breaches, pass audits, and ship changes safely.

Where this fits in our model:
πŸ”’ Security (Semantics) β†’ Cybersecurity β€’ πŸ” Per-Session Access β†’ ZTNA / SASE
πŸ‘€ Identity/Posture β†’ IAM / SSO / MFA β€’ MDM / UEM β€’ EDR / MDR / XDR
πŸ–§ Fabric β†’ Networks & Data Centers β€’ πŸ”€ Transport β†’ SD-WAN β€’ πŸšͺ Edge β†’ NAC
πŸ“Š Evidence & Automation β†’ SIEM / SOAR β€’ πŸ–§ East–West Detection β†’ NDR


🎯 Outcomes (Why Microsegmentation)

  • Containment by default β€” block lateral movement; allow only required flows.
  • Identity- & posture-aware β€” rules bind to user/group, workload/service, and device health.
  • Uniform model β€” one least-privilege approach across DC, cloud, K8s, campus, and remote.
  • Audit-ready β€” policy as code, change diffs, enforcement logs to SIEM.
  • Safer changes β€” staged rollouts, simulation/dry-run, automatic rollback via SOAR.

🧭 Scope (What We Segment)

  • User β†’ App (Access Edge) β€” staff/contractors to specific apps only, never to subnets. β†’ ZTNA β€’ SASE
  • Workload β†’ Workload (East–West) β€” app tiers (web↔app↔DB), microservices, API backends.
  • Zone β†’ Zone (Macro) β€” PCI/PHI/CUI zones, admin/privileged networks, OT/IT boundaries.
  • Device Classes β€” corp laptops vs BYOD; IoT/OT (cameras, printers, POS, sensors) isolated by purpose. β†’ NAC

🧱 Building Blocks (Spelled Out)

  • Policy Abstractions β€” define intentions as Service β†’ Service, Role β†’ App, Label β†’ Label, not IPs.
  • Identity Signals β€” user/group (IdP), device posture (UEM/EDR), service identity (mTLS cert/SPIFFE). β†’ IAM / SSO / MFA β€’ MDM / UEM β€’ EDR / MDR / XDR β€’ PKI
  • Enforcement Planes
  • Host-based (agent/EBPF) β€” kernel firewall rules by process/service identity.
  • Overlay (service mesh) β€” sidecar/intent (L7/K8s).
  • Network (fabric/SGT/VLAN/ACL/VXLAN) β€” fabric tags & ACLs for macro + IoT/OT.
  • Edge (ZTNA/SASE) β€” per-session allow to a single app.
  • Visibility & Evidence β€” flow maps from NDR/NetFlow; change/effect logs to SIEM/SOAR. β†’ NDR β€’ SIEM / SOAR

🧠 Policy Model (Identity β†’ Device β†’ App β†’ Data β†’ Context)

A rule compiles from intent to controls using five lenses:

  1. Identity β€” user/service role (group, SPIFFE ID, certificate SAN).
  2. Device posture β€” EDR/UEM healthy, encryption on, OS β‰₯ min.
  3. Application β€” target service/API/port/protocol with L7 awareness.
  4. Data sensitivity β€” stricter controls for PCI/PHI/CUI; log & watermark on egress. β†’ DLP
  5. Context β€” site/region/ASN, time window, ticket/change ID, session risk.

Decision outcomes: allow (least-privilege) β†’ step-up (MFA/approval) β†’ isolate (quarantine/read-only) β†’ deny.


πŸ”§ Controls (Concrete & Enforceable)

  • Allow-list by default β€” explicit intents only; no β€œany any”.
  • Service Identity β€” mTLS between services; L7 policy (HTTP method, path, GraphQL schema). β†’ Encryption β€’ PKI
  • Host Enforcement β€” per-process rules (linux eBPF, Windows Filtering Platform); block inter-tier except declared.
  • Fabric Tags β€” SGT/labels mapped to ACLs across WAN/DC; carry identity through SD-WAN. β†’ SD-WAN
  • IoT/OT Isolation β€” profile β†’ tag β†’ minimal allow; deny east-west; mgmt only from jump hosts.
  • Quarantine β€” NAC/SD-WAN reclassify on risk; ZTNA cuts user session; EDR isolates host. β†’ NAC β€’ ZTNA β€’ EDR / MDR / XDR

☁️ Cloud & Kubernetes Patterns

  • Cloud VPC/VNet β€” security groups + NACLs compiled from labels (env:prod, tier:api, data:restricted).
  • Kubernetes β€” NetworkPolicies + service mesh policies; SPIFFE/SVID identity; sidecar mTLS; deny pod-to-pod except intent.
  • Hybrid β€” unify labels/IDs across DC and cloud; publish the same intents to multiple enforcement backends. β†’ Cloud β€’ Networks & Data Centers

πŸ”Ž Visibility & Assurance

  • Flow Discovery β€” learn current comms via NDR/flow; generate candidate intents from observed safe flows. β†’ NDR
  • Simulation / Dry-Run β€” evaluate proposed policies in observe-only; compare allowed/blocked deltas.
  • Change as Code β€” policies in git; CI tests (reachability/unit tests) before push.
  • Evidence β€” enforcement hits, denies, CoA/quarantine, and rule versions stream to SIEM; SOAR captures approvals & rollback.

πŸ“ SLO Guardrails (Experience & Safety You Can Measure)

SLO / KPITarget (Recommended)Notes
Policy compile β†’ push (p95)≀ 60–120 sFrom PR approve to enforcement live
Simulation coverageβ‰₯ 95% of flows in dry-runBefore enforce
False-deny rate (after enforce)≀ 1–2%Post-tuning target
Containment (host quarantine)≀ 2–5 min from alertEDR/NAC/ZTNA action
Evidence completeness100% policy versions & hitsLogs to SIEM/SOAR
Platform availabilityβ‰₯ 99.95–99.99%Controllers/agents

SLO breaches trigger SOAR auto-rollback or relaxation of the impacted rules. β†’ SIEM / SOAR


πŸ› οΈ Implementation Blueprint (No-Surprise Rollout)

  1. Inventory & Label β€” apps/services, tiers, environments, data classes; assign labels (env/tier/data/role).
  2. Baseline Flows β€” capture with NDR/NetFlow; map required comms; flag risky unknown flows. β†’ NDR
  3. Write Intents β€” β€œrole X β†’ service Y on port/protocol Z”; β€œservice A ↔ service B (L7 path/verb)”.
  4. Simulate β€” run in observe-only; reconcile denies; commit policy as code with approvals.
  5. Enforce (Rings) β€” canary services/sites β†’ region rings β†’ global; monitor SLOs; auto-rollback on regression.
  6. Edge & User β€” pair with ZTNA/SASE to keep users on app-only paths; deny subnet access. β†’ ZTNA β€’ SASE
  7. Operate & Tune β€” weekly review of denies/allow drift; retire legacy β€œany any”; publish diffs/RCAs.

🧭 Reference Patterns (By Outcome)

A) PCI Cardholder Data Environment (CDE)

  • Macro isolate CDE; micro-allow only app↔DB; tokenization for PAN; ZTNA for admin consoles; logs to SIEM for PCI DSS 10. β†’ DLP

B) PHI / Clinical Apps

  • Web↔app↔DB only; break-glass paths with PAM approval + recording; immutable logs for HIPAA. β†’ PAM

C) Dev/Test vs Prod

  • Deny devβ†’prod; allow CI/CD only via signer/proxy; mTLS service identity; short-lived certs. β†’ PKI

D) IoT/OT Edge

  • Function-based tags; mgmt-only ACLs; no east-west; NDR watch; NAC quarantine on anomaly. β†’ NAC

πŸ“œ Compliance Mapping (Examples)

  • PCI DSS β€” segmentation of CDE, least-privilege, access & log evidence.
  • HIPAA β€” minimum-necessary network access, audit controls.
  • ISO 27001 β€” A.13 network security; A.12 operations; A.16 incident mgmt.
  • NIST 800-53/171 β€” AC/SC families (least privilege, boundary protection).
  • CMMC β€” CUI enclave segmentation, auditing of allows/denies.

All enforcement events & changes stream to SIEM; SOAR automates rollback and containment.


πŸ”„ Where Microsegmentation Fits (Recursive View)

1) Grammar β€” flows traverse Connectivity & Networks & Data Centers.
2) Syntax β€” deployment patterns in Cloud & K8s drive enforcement choices.
3) Semantics β€” Cybersecurity preserves truth; microsegmentation proves least privilege.
4) Pragmatics β€” SolveForce AI predicts risky flows and suggests policy diffs.
5) Foundation β€” consistent terms via Primacy of Language.
6) Map β€” indexed across the SolveForce Codex & Knowledge Hub.


πŸ“ž Deploy Microsegmentation That’s Safe, Measurable & Auditable

Related pages:
Cybersecurity β€’ ZTNA β€’ SASE β€’ NAC β€’ SD-WAN β€’ NDR β€’ EDR / MDR / XDR β€’ SIEM / SOAR β€’ Cloud β€’ Networks & Data Centers β€’ Knowledge Hub


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