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🗺 Product Roadmap

This roadmap outlines the evolution of ChildSafeNet from early prototype to a stable, competition-ready AI safety platform.

Product EvolutionAI LifecycleDeployment Ready

🏗 Architecture

📋 Product Backlog

🧠 AI Training

1. Roadmap Overview​

This roadmap outlines the evolution of ChildSafeNet from core prototype to final submission and beyond.

The roadmap is structured by major versions to reflect architectural growth, AI maturity, and operational stability.

2. Product Versions​

1
v1.0

Core Web (No Extension)

Focus: Foundational backend + web dashboard.

Goals

  • Establish secure authentication
  • Implement basic scan functionality
  • Store logs in database
  • Provide parent configuration

Features

  • JWT-based Authentication
  • Parent Settings (Strict / Balanced / Relaxed)
  • Manual URL Scan page
  • Scan Logs dashboard
  • SQL Server schema (Users, Settings, Logs)

Outcome

Fully working Web + API system without browser automation.

2
v2.0

Extension Integration

Focus: Real-time browser enforcement.

Goals

  • Integrate MV3 browser extension
  • Enable Web ↔ Extension pairing
  • Implement automatic blocking

Features

  • MV3 Extension (Chrome/Edge)
  • Pair token flow (window.postMessage → background)
  • Real-time /api/scan calls
  • block.html for blocked pages
  • Mode synchronization between Web and Extension

Outcome

End-to-end protection: Navigation → API → AI → Decision → Block → Log.

3
v3.0

Final Submission Edition

Focus: AI lifecycle + documentation + operational maturity.

Goals

  • Introduce controlled retraining
  • Improve model governance
  • Prepare competition-ready documentation
  • Automate CI pipeline

Features

  • Admin Dataset Review (Pending / Approved / Rejected)
  • Periodic Training (Option Periodic)
  • Model Registry with versioning
  • Safe activation + rollback logic
  • Docs site (Docusaurus)
  • Architecture diagrams
  • Release checklist templates
  • GitHub Actions CI workflow

Outcome

Stable, auditable, competition-ready AI safety system.

3. Post-Submission Vision​

Focus: Scalability and production-readiness.

Cloud Deployment​

  • Azure App Service for API
  • Azure SQL Database
  • Blob storage for model artifacts
  • HTTPS + managed identity

Model Monitoring​

  • Drift detection metrics
  • False-positive tracking dashboard
  • Auto-switch to fallback model
  • Performance telemetry

Mobile Parent App​

  • View logs on mobile
  • Push notifications for blocked sites
  • Real-time mode toggling

Future AI Enhancements​

  • Online learning experimentation
  • ROC curve-based threshold tuning
  • Explainability UI (feature importance)
  • Threat intelligence feeds integration

4. Versioning Philosophy​

ChildSafeNet follows semantic versioning:

MAJOR.MINOR.PATCH

  • MAJOR: Architectural change or new subsystem
  • MINOR: Feature addition
  • PATCH: Bug fixes / small improvements

5. Long-Term Goal​

Build a trustworthy, explainable, and scalable child internet safety platform that prioritizes:

  • Stability over rapid change
  • Precision-first safety
  • Auditability
  • Safe AI deployment lifecycle

This roadmap is iterative and may evolve based on research, feedback, and deployment constraints.