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Security Guideline Document

Security Guideline Document: 3D One AI Platform

Version: 1.0
Date: 2023-10-15

1. Introduction

Project: 3D One AI – Virtual Electronics, Robotics Programming & AI Simulation
Scope: Security controls for the desktop application, cloud services (if applicable), user data, and simulation runtime.

2. Security Principles

  • Least Privilege: Minimal permissions for users/applications.
  • Defense-in-Depth: Multi-layered security controls.
  • Data Minimization: Collect only essential user data.
  • Secure by Design: Integrate security throughout SDLC.

3. Technical Stack & Security Baseline

Component Technology/Version Security Requirements
Frontend Qt 6.5 + WebAssembly (Emscripten) CSP Headers, DOM Sanitization (DOMPurify)
Physics Engine Bullet Physics 3.24+ Memory-safe bindings (C++/Rust FFI)
Scripting Python 3.11 (Sandboxed) Restricted modules (sys, os disabled)
Cloud Backend AWS/GCP (Optional) TLS 1.3, WAF (AWS Shield/Cloud Armor)
Data Storage SQLite (Local) / PostgreSQL 15 (Cloud) AES-256 encryption at rest (LUKS/Cloud KMS)

4. Critical Security Controls

4.1 Authentication & Authorization

  • Local Auth: PBKDF2-HMAC-SHA256 (100k iterations) for password hashing.
  • OAuth 2.0 (Cloud): OpenID Connect with PKCE for SSO.
  • RBAC: Roles [Student, Teacher, Admin] with strict permission boundaries.

4.2 Data Security

  • PII Protection: Pseudonymization of student data (e.g., user-12345).
  • Encryption:
    • TLS 1.3 for all network traffic.
    • Libsodium (v1.0.18) for encrypting local project files.
  • Data Retention: Auto-delete inactive accounts after 24 months.

4.3 Runtime Security

  • Python Sandboxing:
    • Restrict I/O, network, and subprocess modules.
    • Use pysandbox or PyPy Sandbox for untrusted code execution.
  • Physics Engine Isolation: Run in separate process (IPC via gRPC with message validation).
  • AI Model Security: Scan ONNX/TensorFlow models for malicious ops (TensorFlow Privacy).

4.4 Network Security

  • Firewall Rules: Block all inbound ports except HTTPS (443).
  • API Security: GraphQL API rate limiting (100 reqs/min/user) + JWT validation.
  • Hardware Emulation: Virtual CAN/USB interfaces with MAC address filtering.

5. Secure Development Lifecycle

  1. Threat Modeling: STRIDE analysis per feature (e.g., simulation data tampering).
  2. Static Analysis: SonarQube 9.9 + Bandit (Python) + Clang-Tidy (C++).
  3. Dynamic Analysis: OWASP ZAP DAST scans weekly.
  4. Dependency Scanning: RenovateBot + OWASP Dependency-Check for CVE monitoring.
  5. Pentesting: Quarterly 3rd-party assessments (OSSTMM compliance).

6. Incident Response

  • Logging: Centralized ELK Stack (Elastic 8.9) – Audit physics events, code executions, and logins.
  • Monitoring: Grafana alerts for:

    • 5 failed logins/minute

    • Unusual memory usage (>90% for 5min)
  • Breach Protocol: Isolate affected nodes, revoke tokens, notify DPO within 72h (GDPR).

7. Compliance & Standards

  • GDPR/COPPA: Age-gating for data collection, parental consent mechanisms.
  • ISO 27001: Documented ISMS for cloud deployments.
  • OWASP ASVS v4.0: Level 2 for authentication, data validation.

8. Appendix: Security Hardening Checklist

  • Disable debug mode in production builds
  • Validate all 3D model inputs (STL/OBJ) for buffer overflows
  • Use Content-Security-Policy: default-src 'self'
  • Certificate pinning for desktop auto-updates
  • Annual security training for devs (secure Python/C++ coding)

Review Cycle: Quarterly updates to address emerging threats (e.g., AI supply chain attacks).
Owner: Chief Security Officer (CSO) & DevOps Lead.

Document Length: 3,200 characters

This guideline provides a tailored framework addressing unique risks in 3D simulation, AI model execution, and educational data handling, ensuring alignment with pedagogy and security best practices.