Implementing Microsegmentation With Guardicore

Implementing Microsegmentation with Guardicore

When to Use

• When implementing east-west traffic controls to prevent lateral movement within data centers
• When needing application-level visibility into network communication patterns before writing segmentation policies
• When segmenting workloads across heterogeneous environments (VMs, containers, bare metal, cloud)
• When compliance frameworks (PCI DSS, HIPAA) require network segmentation validation
• When deploying zero trust at the network layer with process-level granularity

Do not use for perimeter-only security (use traditional firewalls), for environments with fewer than 50 workloads where VLANs/security groups suffice, or when network team lacks capacity for ongoing policy management.

Prerequisites

• Akamai Guardicore Segmentation license (Enterprise or Premium)
• Guardicore Management Server deployed (on-prem or SaaS)
• Agent deployment access to target workloads (Linux, Windows, Kubernetes)
• Network visibility: SPAN/TAP ports or VPC flow logs for agentless collection
• Application owner engagement for dependency validation

Workflow

Step 1: Deploy Guardicore Agents on Workloads

Install agents to collect process-level network communication data.

# Linux agent installation
curl -sSL https://management.guardicore.com/api/v3.0/agents/download/linux \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -o gc-agent-installer.sh
chmod +x gc-agent-installer.sh
sudo ./gc-agent-installer.sh \
  --management-url=https://management.guardicore.com \
  --site-id=datacenter-east \
  --label="web-tier"

# Windows agent installation (PowerShell)
# Invoke-WebRequest -Uri "https://management.guardicore.com/api/v3.0/agents/download/windows" `
#   -Headers @{"Authorization"="Bearer $GC_API_TOKEN"} `
#   -OutFile gc-agent-installer.exe
# Start-Process -FilePath .\gc-agent-installer.exe `
#   -ArgumentList "--management-url=https://management.guardicore.com","--site-id=datacenter-east" `
#   -Wait

# Kubernetes DaemonSet deployment
cat > gc-daemonset.yaml << 'EOF'
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: guardicore-agent
  namespace: guardicore
spec:
  selector:
    matchLabels:
      app: gc-agent
  template:
    metadata:
      labels:
        app: gc-agent
    spec:
      hostNetwork: true
      hostPID: true
      containers:
      - name: gc-agent
        image: guardicore/agent:latest
        securityContext:
          privileged: true
        env:
        - name: GC_MANAGEMENT_URL
          value: "https://management.guardicore.com"
        - name: GC_API_KEY
          valueFrom:
            secretKeyRef:
              name: gc-credentials
              key: api-key
        volumeMounts:
        - mountPath: /host
          name: host-root
      volumes:
      - name: host-root
        hostPath:
          path: /
EOF
kubectl apply -f gc-daemonset.yaml

# Verify agent enrollment
curl -s "https://management.guardicore.com/api/v3.0/agents?status=active" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" | python3 -m json.tool

Step 2: Map Application Dependencies with Reveal

Use Guardicore Reveal to discover and visualize application communication patterns.

# Query discovered application flows via API
curl -s "https://management.guardicore.com/api/v3.0/connections" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -d '{
    "time_range": {"from": "2026-02-17T00:00:00Z", "to": "2026-02-24T00:00:00Z"},
    "filter": {
      "source_label": "web-tier",
      "destination_label": "app-tier"
    },
    "aggregation": "process",
    "limit": 1000
  }' | python3 -m json.tool

# Export application dependency map
curl -s "https://management.guardicore.com/api/v3.0/maps/export" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -d '{
    "format": "json",
    "labels": ["web-tier", "app-tier", "db-tier"],
    "time_range": "7d"
  }' -o app-dependency-map.json

# Typical discovery findings:
# web-tier -> app-tier: TCP 8080, 8443 (expected)
# app-tier -> db-tier: TCP 5432, 3306 (expected)
# web-tier -> db-tier: TCP 5432 (UNEXPECTED - should be blocked)
# app-tier -> internet: TCP 443 (verify if needed)

Step 3: Create Segmentation Labels and Policies

Define labels and create ring-fence policies around applications.

# Create labels for application tiers
curl -X POST "https://management.guardicore.com/api/v3.0/labels" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "PCI-CDE",
    "description": "Cardholder Data Environment workloads",
    "criteria": {"ip_ranges": ["10.10.0.0/16"]},
    "color": "#FF0000"
  }'

# Create segmentation policy: Allow web-to-app communication
curl -X POST "https://management.guardicore.com/api/v3.0/policies" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Web-to-App Allowed",
    "action": "ALLOW",
    "priority": 100,
    "source": {"labels": ["web-tier"]},
    "destination": {"labels": ["app-tier"]},
    "services": [
      {"protocol": "TCP", "port": 8080},
      {"protocol": "TCP", "port": 8443}
    ],
    "log": true,
    "enabled": true,
    "section": "application-segmentation"
  }'

# Create deny policy: Block web-to-database direct access
curl -X POST "https://management.guardicore.com/api/v3.0/policies" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Block Web-to-DB Direct",
    "action": "DENY",
    "priority": 200,
    "source": {"labels": ["web-tier"]},
    "destination": {"labels": ["db-tier"]},
    "services": [{"protocol": "TCP", "port_range": "1-65535"}],
    "log": true,
    "alert": true,
    "enabled": true
  }'

# Create ring-fence policy for PCI CDE
curl -X POST "https://management.guardicore.com/api/v3.0/policies" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "PCI CDE Ring Fence",
    "action": "DENY",
    "priority": 50,
    "source": {"labels": ["!PCI-CDE"]},
    "destination": {"labels": ["PCI-CDE"]},
    "services": [{"protocol": "TCP", "port_range": "1-65535"}],
    "log": true,
    "alert": true,
    "enabled": true
  }'

Step 4: Test Policies in Reveal Mode Before Enforcement

Simulate policy enforcement without blocking traffic.

# Enable reveal mode (log-only) for new policies
curl -X PATCH "https://management.guardicore.com/api/v3.0/policies/POLICY_ID" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -d '{"enforcement_mode": "REVEAL"}'

# Check what would be blocked in reveal mode
curl -s "https://management.guardicore.com/api/v3.0/violations" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -d '{
    "time_range": "24h",
    "policy_id": "POLICY_ID",
    "limit": 100
  }' | python3 -c "
import json, sys
data = json.load(sys.stdin)
for v in data.get('violations', []):
    print(f\"{v['source_ip']}:{v['source_process']} -> {v['dest_ip']}:{v['dest_port']} [{v['action']}]\")
"

# After validation, switch to enforcement
curl -X PATCH "https://management.guardicore.com/api/v3.0/policies/POLICY_ID" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -d '{"enforcement_mode": "ENFORCE"}'

Step 5: Monitor and Respond to Policy Violations

Set up alerting and continuous monitoring for segmentation violations.

# Configure SIEM integration for policy violations
curl -X POST "https://management.guardicore.com/api/v3.0/integrations/syslog" \
  -H "Authorization: Bearer ${GC_API_TOKEN}" \
  -d '{
    "name": "Splunk SIEM",
    "host": "splunk-syslog.company.com",
    "port": 514,
    "protocol": "TCP",
    "format": "CEF",
    "events": ["policy_violation", "agent_status", "deception_alert"]
  }'

# Splunk query for microsegmentation violations
# index=guardicore sourcetype=guardicore:policy
# | where action="DENY" AND enforcement_mode="ENFORCE"
# | stats count by src_ip, dst_ip, dst_port, policy_name
# | sort -count

Key Concepts

Tools & Systems

• Akamai Guardicore Segmentation: Agent-based microsegmentation platform with application visualization and policy enforcement
• Guardicore Reveal: Network visualization engine mapping application dependencies across hybrid environments
• Guardicore Centra: Management console for policy creation, monitoring, and incident investigation
• Guardicore Agents: Lightweight agents deployed on workloads collecting process-level network telemetry
• Guardicore Insight: Analytics engine for compliance reporting and segmentation effectiveness measurement

Common Scenarios

Scenario: PCI DSS Microsegmentation for E-Commerce Platform

Context: An e-commerce company must isolate its Cardholder Data Environment (CDE) from the rest of the corporate network for PCI DSS compliance. The CDE spans 200 servers across on-prem and AWS.

Approach:

1. Deploy Guardicore agents on all 200 CDE servers and 300 non-CDE servers
2. Run Reveal for 2 weeks to map all communication patterns into and out of the CDE
3. Identify and remediate unexpected flows (e.g., dev servers connecting to production CDE)
4. Create ring-fence policy blocking all non-CDE to CDE traffic by default
5. Create explicit allow policies for validated CDE communication paths
6. Test in Reveal mode for 1 week, validate no legitimate traffic blocked
7. Switch to enforcement mode and monitor for violations
8. Generate PCI DSS segmentation validation report showing enforced controls

Pitfalls: Agent deployment on legacy systems (Windows Server 2012) may require manual installation. Ring-fence policies must account for management traffic (monitoring, patching, backup). Start with broad allow rules and progressively tighten. Application owners must validate dependency maps before enforcement.

Output Format

Microsegmentation Deployment Report
==================================================
Organization: E-Commerce Corp
Report Date: 2026-02-23

AGENT DEPLOYMENT:
  Total workloads:            500
  Agents installed:           487 (97.4%)
  Agents active:              482 (98.9%)
  Agentless (flow logs):       13

POLICY COVERAGE:
  Total policies:              45
  Allow rules:                 38
  Deny rules:                   7
  Reveal mode:                  3
  Enforced:                    42

TRAFFIC ANALYSIS (7 days):
  Total flows observed:        2,456,789
  Flows matching allow:        2,441,234 (99.4%)
  Flows matching deny:            15,555 (0.6%)
  Unclassified flows:                 0

PCI CDE ISOLATION:
  CDE workloads:               200
  Ring-fence violations:         0 (last 30 days)
  Authorized CDE entry points:  4
  Lateral movement paths blocked: 95%