Agent skill

Implementing Microsegmentation With Guardicore

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs…

Apache-2.0Auto-check: notesLegal & Compliance

Install Implementing Microsegmentation With Guardicore

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicore --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .claude/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
implementing-microsegmentation-with-guardicore
GitHub stars
34k
Token cost
~3.1k tokens
SKILL.md length
562 words
Files
4 (incl. scripts, references)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs…

  • Works in 5 steps: Deploy Guardicore Agents on Workloads → Map Application Dependencies with Reveal → Create Segmentation Labels and Policies → …
  • Blocking lateral movement in a data center
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls curl, python3 and kubectl; reaches management.guardicore.com; needs GC_API_TOKEN and GC_API_KEY

What it does

Implementing Microsegmentation With Guardicore is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs, containers, bare metal, and cloud. Use when blocking lateral movement in a data center or when PCI DSS/HIPAA compliance requires validated network segmentation.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Legal & Compliance, covering Healthcare and finance regulation and Red teaming and adversary simulation. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Blocking lateral movement in a data center
  • PCI DSS/HIPAA compliance requires validated network segmentation

Example prompts

  • “Use the implementing-microsegmentation-with-guardicore skill to implement microsegmentation with Akamai Guardicore Segmentation to map application…”
  • “/implementing-microsegmentation-with-guardicore”

Requirements

  • Python 3
  • A credential in GC_API_TOKEN
  • A credential in GC_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Deploy Guardicore Agents on Workloads
  2. Map Application Dependencies with Reveal
  3. Create Segmentation Labels and Policies
  4. Test Policies in Reveal Mode Before Enforcement
  5. Monitor and Respond to Policy Violations

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • curl
    • python3
    • kubectl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • management.guardicore.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GC_API_TOKEN
    • GC_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Implementing Microsegmentation With Guardicore loads about 3.1k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 562 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:68
    sudo ./gc-agent-installer.sh \

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 562 words, ~3,111 tokens.

Download SKILL.mdSave it as .claude/skills/implementing-microsegmentation-with-guardicore/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
implementing-microsegmentation-with-guardicore
description
Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs, containers, bare metal, and cloud. Use when blocking lateral movement in a data center or when PCI DSS/HIPAA compliance requires validated network segmentation.
domain
cybersecurity
subdomain
zero-trust-architecture
tags
microsegmentation, guardicore, akamai, zero-trust, east-west-traffic, network-segmentation, lateral-movement
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
PR.AA-01, PR.AA-05, PR.IR-01, GV.PO-01
mitre_attack
T1078, T1190, T1059, T1021, T1550

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.

bash
# 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.

bash
# 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.

bash
# 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.

bash
# 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.

bash
# 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

TermDefinition
MicrosegmentationNetwork security technique creating granular security zones around individual workloads or applications to control east-west traffic
Reveal ModeGuardicore's simulation mode that logs policy decisions without enforcing them, allowing validation before blocking
Ring-Fence PolicyIsolation policy that restricts all traffic into or out of a defined group of assets (e.g., PCI CDE)
Application Dependency MapVisual representation of discovered network communication patterns between workloads showing processes, ports, and protocols
East-West TrafficNetwork traffic flowing laterally between workloads within a data center, as opposed to north-south traffic crossing the perimeter
Process-Level VisibilityGuardicore's ability to identify which process on a workload initiated or received a network connection
Show full SKILL.md (237 more words)Show less

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%

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/implementing-microsegmentation-with-guardicore of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Questions about Implementing Microsegmentation With Guardicore

What does Implementing Microsegmentation With Guardicore do?

Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs…. Implementing Microsegmentation With Guardicore is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs, containers, bare metal, and cloud.

When should I use Implementing Microsegmentation With Guardicore?

Implementing Microsegmentation With Guardicore fits situations like: blocking lateral movement in a data center; PCI DSS/HIPAA compliance requires validated network segmentation.

How do I install Implementing Microsegmentation With Guardicore in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a claude-code`. Or copy the skill folder (skills/implementing-microsegmentation-with-guardicore in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/implementing-microsegmentation-with-guardicore in your project. Claude Code loads it when a task matches its description.

How do I install Implementing Microsegmentation With Guardicore in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a codex`. Or copy the skill folder (skills/implementing-microsegmentation-with-guardicore in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/implementing-microsegmentation-with-guardicore in your project. Codex loads it when a task matches its description.

Can I use Implementing Microsegmentation With Guardicore in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/implementing-microsegmentation-with-guardicore, .gemini/skills/implementing-microsegmentation-with-guardicore, .github/skills/implementing-microsegmentation-with-guardicore and .opencode/skills/implementing-microsegmentation-with-guardicore in your project.

What does Implementing Microsegmentation With Guardicore need to run?

Going by SKILL.md and its folder, Implementing Microsegmentation With Guardicore needs Python for the scripts in its folder, the command-line tools its instructions call (curl, python3 and kubectl) and credentials named GC_API_TOKEN and GC_API_KEY. Our summary lists: Python 3; A credential in GC_API_TOKEN; A credential in GC_API_KEY.

Does Implementing Microsegmentation With Guardicore access the network?

SKILL.md names 1 domain. In commands or code: management.guardicore.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Implementing Microsegmentation With Guardicore safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Implementing Microsegmentation With Guardicore use?

Implementing Microsegmentation With Guardicore is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Implementing Microsegmentation With Guardicore use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 422 tokens, read only when the agent opens those files.

What are the alternatives to Implementing Microsegmentation With Guardicore?

Skills that share tags, products or a category with Implementing Microsegmentation With Guardicore: HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), HIPAA Pre-Deployment Compliance Check (maziyarpanahi/openmed, 5.5k stars), Hipaa Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 942 stars) and ISO Standards Readiness Evidence (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Implementing Microsegmentation With Guardicore?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.