Artificial Intelligence Security Reference Architecture & Security Consultancy Services Work Package

1. Purpose of the Work Package

To provide organisations with a complete, secure, and responsible framework for adopting Artificial Intelligence. This work package delivers:

• A full AI Security Reference Architecture

• A structured consultancy service offering

• A modular delivery methodology

• Clear outputs, artefacts, and deliverables

• A roadmap for secure, compliant, ethical AI adoption

This is designed for charities, SMEs, public‑sector teams, and enterprise clients.

2. Consultancy Outcomes

By the end of the engagement, clients will have:

• A tailored AI Security Reference Architecture

• A full assessment of AI risks, controls, and governance gaps

• A secure AI operating model aligned with Zero Trust

• A responsible AI governance framework

• A secure data, model, and API architecture

• A roadmap for secure AI adoption

• A set of actionable recommendations and implementation steps

3. AI Security Reference Architecture (Core Framework)

The architecture is built on eight pillars, each representing a critical security domain.

Pillar/Focus Area Outcome

Governance & Responsible AI Ethical, transparent, accountable AI use

Identity & Access Security Strong protection for AI systems and users

Data Security & Privacy Secure, compliant, minimal data use

Model Security Protection of models from theft, misuse, and manipulation

Application & API Security Secure AI endpoints and integrations

Infrastructure & Cloud Security Hardened, monitored, Zero Trust‑aligned environments

Monitoring, Detection  Continuous oversight and threat detection

Lifecycle Management Secure development, deployment, and maintenance

Each pillar includes principles, controls, and implementation guidance.

4. Detailed Architecture Breakdown

Pillar 1 — Governance & Responsible AI

Principles

• Transparency

• Accountability

• Human oversight

• Fairness

• Explainability

Controls

• Responsible AI Policy

• AI Risk Assessments

• Model documentation (datasheets, model cards)

• Oversight roles (AI Lead, Data Protection Lead)

Consultancy Deliverables

• Governance Framework

• Responsible AI Charter

• AI Risk Register

Pillar 2 — Identity & Access Security

Controls

• MFA everywhere

• Role‑based access control

• Just‑in‑time privileged access

• Conditional access

• Identity governance for AI endpoints

Consultancy Deliverables

• Identity & Access Control Matrix

• Secure Access Pathway

• Privileged Access Review

Pillar 3 — Data Security & Privacy

Controls

• Data minimisation

• Encryption in transit and at rest

• Secure data pipelines

• Privacy‑preserving techniques

• Data classification for AI workloads

Consultancy Deliverables

• Data Protection Checklist

• Secure Data Pipeline Architecture

• Data Flow Mapping

Pillar 4 — Model Security

Threats

• Model theft

• Model inversion

• Data poisoning

• Adversarial inputs

• Prompt injection

Controls

• Model access control

• Model watermarking

• Adversarial testing

• Content safety layers

• Secure model storage

Consultancy Deliverables

• Model Security Framework

• Adversarial Testing Plan

• Model Access Governance

Pillar 5 — Application & API Security

Controls

• API gateways

• Rate limiting

• Input validation

• Output filtering

• Abuse detection

Consultancy Deliverables

• Secure AI API Architecture

• API Abuse Prevention Guide

• API Security Testing Report

Pillar 6 — Infrastructure & Cloud Security

Controls

• Zero Trust segmentation

• Private endpoints

• Secrets management

• Secure containers

• Cloud posture management

Consultancy Deliverables

• Cloud AI Security Blueprint

• Infrastructure Hardening Checklist

• Secure Deployment Architecture

Pillar 7 — Monitoring, Detection & Response

Controls

• Logging and audit trails

• Behavioural analytics

• Threat detection rules

• Incident response playbooks

Consultancy Deliverables

• AI Security Monitoring Framework

• Incident Response Playbook

• Threat Detection Ruleset

Pillar 8 — Lifecycle Management & MLOps Security

Controls

• Version control

• Model lineage tracking

• Secure CI/CD pipelines

• Testing and validation

• Continuous monitoring

Consultancy Deliverables

• MLOps Security Framework

• Secure Deployment Checklist

• Model Lifecycle Governance

5. Consultancy Services Work Package

This is the client‑facing service offering you can deliver.

Phase 1 — Discovery & Assessment

Activities

• AI maturity assessment

• AI risk assessment

• Data flow mapping

• Identity & access review

• Cloud posture review

Outputs

• AI Security Assessment Report

• Gap analysis

• Risk register

Phase 2 — Architecture & Design

Activities

• Design of AI Security Reference Architecture

• Data, model, and API security design

• Governance and responsible AI framework

• Zero Trust alignment

Outputs

• Full AI Security Reference Architecture

• Governance & Responsible AI Framework

• Secure AI Operating Model

Phase 3 — Implementation Planning

Activities

• Prioritisation of controls

• Roadmap creation

• Implementation sequencing

• Budget and resource planning

Outputs

• AI Security Roadmap

• Implementation Plan

• Control Prioritisation Matrix

Phase 4 — Enablement & Training

Activities

• Executive briefings

• Technical deep‑dives

• Staff training

• Secure AI usage workshops

Outputs

• Training materials

• Secure AI Usage Guide

• AI Security Playbooks

Phase 5 — Ongoing Advisory (Optional)

Activities

• Quarterly reviews

• Architecture updates

• Threat landscape updates

• Governance refresh

Outputs

• Quarterly AI Security Report

• Updated risk register

• Updated architecture artefacts

6.  Deliverables Summary

• AI Security Reference Architecture

• Governance & Responsible AI Framework

• Identity & Access Control Matrix

• Secure Data Pipeline Architecture

• Model Security Framework

• Secure AI API Architecture

• Cloud AI Security Blueprint

• AI Security Monitoring Framework

• MLOps Security Framework

• AI Security Roadmap

• Executive Summary