Micro Segmentation Policy

Micro Segmentation Policy

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Micro Segmentation Policy divides IoT infrastructure into small, isolated security zones with strict access controls between them. This limits lateral movement, contains breaches, and implements zero-trust principles at scale.

Why This Matters

• :
• :
• :

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • <e.g., env vars, request payload, file paths, schema>
• Entry Conditions:
  • <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
• Outputs:
  • <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
• Artifacts Required (Deliverables):
  • <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
• Acceptance Evidence:
  • <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
• Success Criteria:
  • <e.g., p95 < 300ms, coverage ≥ 80%>

Skill Composition

• Depends on: security
• Compatible with: None
• Conflicts with: None
• Related Skills: authn, authz

Quick Start

1. Install dependencies:

   pip install netaddr ipaddress
   

2. Initialize segmentation manager:

   manager = SegmentationManager(
       enforcement_mode=PolicyMode.ENFORCE,
       default_action=PolicyAction.DENY
   )
   

3. Add security zones:

   manager.add_zone(SecurityZone(
       name="devices",
       subnets=["10.0.10.0/24"]
   ))
   

4. Add segmentation policies:

   manager.add_policy(SegmentationPolicy(
       name="devices_to_data",
       source_zone="devices",
       destination_zone="data",
       allowed_services=["mqtt:1883"],
       action=PolicyAction.ALLOW
   ))
   

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns

1. Flat Network: No segmentation, all devices in same zone

   • Why it's bad: Lateral movement unchecked
   • Solution: Implement micro segmentation

2. Overly Permissive Policies: Allow all traffic between zones

   • Why it's bad: Defeats purpose of segmentation
   • Solution: Implement least privilege policies

3. No Monitoring: Not logging or monitoring traffic

   • Why it's bad: Can't detect violations
   • Solution: Enable comprehensive logging

4. Static Policies: Policies never updated

   • Why it's bad: Doesn't adapt to changes
   • Solution: Regular policy reviews and updates

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure

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