Work Package: Community Reference Security & AI Reference Architectures

1. Purpose & Strategic Intent

This Work Package establishes two foundational, community‑ready reference architectures:

• Community Reference Security Architecture (CRSA)

• Community AI Reference Architecture (CAIRA)

These architectures will provide clear, accessible, evidence‑based models that Scottish communities, charities, mosques, youth groups, small businesses, and public‑sector partners can adopt to strengthen digital resilience, ethical AI adoption, and governance maturity.

The work aligns with:

• National cyber resilience frameworks

• Ethical AI and data governance principles

• Community empowerment and digital inclusion goals

• Your signature frameworks (Industrial Ummah Thinking, governance toolkits, etc.)

2. Scope of Work

2.1 Community Reference Security Architecture (CRSA)

The CRSA will define a modular, scalable security blueprint tailored for community organisations with varying maturity levels.

Core Components

• Security Governance Layer
  
  

  • Roles, responsibilities, trustee oversight

  • Community‑appropriate policies and controls

  • Risk management and threat modelling templates

• Identity & Access Management (IAM)
  
  

  • Zero Trust‑aligned principles

  • MFA, least privilege, role‑based access patterns

  • Volunteer and transient‑staff access models

• Data Protection & Privacy
  
  

  • Data classification scheme

  • Community‑friendly GDPR guidance

  • Secure data lifecycle patterns

• Cloud & Infrastructure Security
  
  

  • Reference patterns for Microsoft 365, Azure, AWS, GCP

  • Secure configuration baselines

  • Network segmentation and endpoint protection

• Application & DevSecOps
  
  

  • Secure SDLC for community tech projects

  • SAST/DAST/IAST integration patterns

  • Open‑source risk management

• Incident Response & Resilience
  
  

  • Community‑ready IR playbooks

  • Crisis communication templates

  • Backup and recovery patterns

2.2 Community AI Reference Architecture (CAIRA)

The CAIRA will define safe, ethical, and accessible AI adoption patterns for community organisations.

Core Components

• AI Governance & Ethics
  
  

  • Principles for fairness, transparency, accountability

  • Community‑aligned AI ethics charter

  • Trustee‑level oversight model

• Data Foundations
  
  

  • Data readiness assessment

  • Community‑safe data collection and storage

  • Bias mitigation and data quality patterns

• AI Capability Layers
  
  

  • LLM usage patterns (assistants, chatbots, summarisation)

  • Predictive analytics for community services

  • Automation and workflow augmentation

• Security & Privacy for AI Systems
  
  

  • Model security, prompt‑injection mitigation

  • Data leakage prevention

  • Access control and auditability

• Deployment & Integration Patterns
  
  

  • Cloud‑native AI services (Azure OpenAI, AWS Bedrock, GCP Vertex)

  • On‑premise / edge AI for sensitive data

  • API integration patterns

• Community Enablement
  
  

  • AI literacy modules

  • Accessible training for youth, trustees, and ASN groups

  • Templates for safe AI usage policies

3. Deliverables

Deliverables - Description

D1. Architecture Blueprint (CRSA)

Full security reference architecture with diagrams, layers, and controls

PDF + diagrams

D2. Architecture Blueprint (CAIRA)

Full AI reference architecture with governance, data, and capability layers

PDF + diagrams

D3. Community Governance Toolkit

Policies, templates, checklists, trustee guidance

Modular toolkit

D4. Training & Enablement Pack

Slide decks, facilitator notes, community‑friendly guides

PPT + workbook

D5. Executive Summary

High‑level narrative for leaders, funders, and boards

2–3 pages

D6. Community Adoption Roadmap

Phased maturity model and implementation plan

Roadmap document

4. Work Breakdown Structure (WBS)

Phase 1 — Discovery & Engagement

• Stakeholder interviews (community leaders, trustees, youth reps)

• Maturity assessment across security and AI

• Requirements gathering

• Review of existing frameworks (NCSC, ISO, NIST, EU AI Act)

Outputs: Discovery report, requirements matrix

Phase 2 — Architecture Design

• Draft CRSA and CAIRA logical and physical layers

• Define governance, security, and AI capability models

• Create diagrams, patterns, and reusable templates

Outputs: Architecture drafts, diagrams, governance models

Phase 3 — Validation & Community Testing

• Workshops with community groups

• Scenario‑based testing (e.g., cyber incident, AI misuse)

• Refinement based on feedback

Outputs: Validated architectures, updated toolkits

Phase 4 — Finalisation & Packaging

• Produce final architecture documents

• Build training materials and facilitator guides

• Create adoption roadmap and maturity model

Outputs: Final deliverables package

Phase 5 — Handover & Capability Building

• Deliver training sessions

• Knowledge transfer to community champions

• Optional: ongoing advisory support

Outputs: Training delivered, handover pack

5. Roles & Responsibilities

Consultant (You)

• Lead architect and governance designer

• Community engagement facilitator

• Technical author and trainer

• Quality assurance and final sign‑off

Community Partners

• Provide access to stakeholders

• Participate in workshops

• Review and validate materials

Technical SMEs (Optional)

• Cloud security

• AI engineering

• Data governance

6. Timeline (Indicative)

Phase Duration

Discovery 2–3 weeks

Architecture Design 4–6 weeks

Validation 2–3 weeks

Finalisation 2 weeks

Handover 1–2 weeks

Total: 11–16 weeks

7. Success Criteria

• Architectures are clear, accessible, and community‑ready

• Materials are modular, reusable, and scalable

• Governance models strengthen trust, safety, and accountability

• AI adoption is ethical, secure, and inclusive

• Communities gain practical capability, not just documents