Edge Policy Enforcer

Go platform engineering portfolio project demonstrating edge request governance, policy evaluation, redirect control, bot handling, geo routing, and traffic-pressure decisions.

Portfolio takeaway: "Platform controls become much more credible when the request-decision layer is treated as a product, not just middleware glue."

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Project Overview

Attribute	Detail
Language	Go
HTTP Layer	net/http
Domain	Edge request governance and policy enforcement
Decision Outputs	allow · challenge · redirect · deny
Operational Themes	bot handling · geo routing · preview protection · rate pressure
Artifacts	dashboard summary · policy inventory · redirect inventory

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Executive Summary

Edge Policy Enforcer models the kind of service platform engineering teams use in front of public origins when request handling needs more nuance than a static CDN rule sheet. Instead of treating edge logic as scattered middleware or a few hard-coded if-statements, the project frames policy enforcement as a centralized control layer that can evaluate risk, route traffic, preserve regional experiences, and protect unreleased surfaces.

The service exposes operational endpoints for policy visibility, evaluates inbound request posture, and returns a clear decision with matched policies, issues, passed checks, and next actions. The repo is designed to feel like infrastructure with product thinking behind it.

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Architecture

Inbound request signal
    |
    v
POST /api/evaluate/*
    |
    +--> request posture classification
    +--> bot and burst evaluation
    +--> geo / redirect lane matching
    +--> preview and release lock checks
    |
    v
Operational action
    |
    +--> allow
    +--> challenge
    +--> redirect
    +--> deny

Governance Workflow

1. Edge or gateway systems submit request posture to the policy engine.
2. The engine evaluates preview exposure, bot confidence, traffic pressure, and routing mismatches.
3. Matching policies are attached to the decision response.
4. Operators use /api/dashboard/summary, /api/policies, /api/origins, and /api/redirect-rules for visibility into the governed edge surface.

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Decision Model

Request Evaluation

Request scoring covers:

• preview or release-locked access
• bot confidence
• suspicious request signals
• regional experience mismatches
• redirect-rule eligibility
• burst traffic pressure

Rate Pressure Review

Rate review catches:

• unsafe requests-per-minute levels
• elevated error-rate pressure
• edge saturation risk
• loss of safe prioritization for critical routes

Bot Decisioning

Bot handling distinguishes:

• normal customer traffic
• challenge-worthy automation
• block-worthy hostile request posture

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API Endpoints

Method	Endpoint	Purpose
GET	/	Service index
GET	/health	Service status
GET	/api/origins	List governed origins
GET	/api/policies	List active policies
GET	/api/redirect-rules	List redirect governance rules
GET	/api/dashboard/summary	Operational summary view
POST	/api/evaluate/request	Run full edge request evaluation
POST	/api/evaluate/rate-pressure	Review burst and saturation posture
POST	/api/evaluate/bot	Evaluate automation risk posture

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Sample Request Evaluation

{
  "originId": "growth-site",
  "path": "/pricing",
  "method": "GET",
  "geo": "GB",
  "language": "en-GB",
  "userAgentClass": "browser",
  "botScore": 22,
  "requestsPerMinute": 64,
  "suspiciousSignals": 0,
  "preview": false,
  "releaseLocked": false,
  "expectedExperienceGeo": "US"
}

Sample Response

{
  "status": "redirect",
  "score": 54,
  "riskLevel": "moderate",
  "matchedPolicies": [
    {
      "id": "geo-lane-redirect",
      "name": "Regional experience redirect",
      "category": "geo",
      "action": "redirect",
      "reason": "Route UK visitors into the localized conversion path."
    }
  ],
  "issues": [
    "The incoming experience lane does not match the governed regional surface."
  ],
  "passedChecks": [
    "Origin and path were evaluated against the current policy set."
  ],
  "recommendedNextAction": "Redirect into the governed regional lane and preserve attribution headers.",
  "redirectTarget": "/uk/pricing"
}

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Screenshots

Hero Capture

Policy Surfaces

Industry Application Layer

Validation Proof

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Getting Started

Prerequisites

• Go 1.22+

Setup

git clone https://github.com/mizcausevic-dev/edge-policy-enforcer.git
cd edge-policy-enforcer
go run ./cmd/server

Visit:

• http://localhost:8080/
• http://localhost:8080/health
• http://localhost:8080/api/dashboard/summary

Run Tests

go test ./...

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What This Demonstrates

• Go backend breadth beyond the broader TypeScript portfolio layer
• request-governance modeling as a platform capability
• traffic-shaping and bot-handling thinking
• infrastructure-minded API design with clear operational outputs
• policy visibility and controlled redirect governance

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Future Enhancements

• signed rule bundles for multi-environment policy promotion
• Redis-backed request counters for live rate enforcement
• per-origin policy overrides with approval workflows
• OpenTelemetry tracing for decision-path observability
• WASM-compatible rule execution for edge deployment targets

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Tech Stack

Portfolio Links

• LinkedIn
• Kinetic Gain
• Skills Page
• GitHub

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Part of mizcausevic-dev's GitHub portfolio, with a focus on platform engineering, request governance, and infrastructure-grade decision systems.