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AI ENGINEERING / SECURE BY DESIGN

Secure AI implementation for organisations

We help organisations deploy useful AI without giving the model more access than the business process requires.

This is an additional service alongside our core security practice. Solution design is combined with threat modelling, access control and pre-production testing so security is not added at the end.

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USE CASE FIRST
Business objective
LEAST PRIVILEGE
Agent access
EVALUATIONS
Quality criteria
OBSERVABILITY
Monitoring
01 / DECISION CONTEXT

Where we help

  • 01Chatbots and assistants using internal knowledge.
  • 02Agents automating CRM, helpdesk or back-office operations.
  • 03Secure model, supplier and data-processing choices.
  • 04Shadow AI governance, usage rules and use-case inventory.
02 / TEST SURFACE

Implementation layers

01

Decision and architecture

We start with process, data and success criteria.

  • Use case, risk and build-versus-buy
  • Supplier, DPA, region and retention
  • RAG, integrations and system boundaries
02

Secure implementation

Control is designed outside the prompt.

  • Identity, tenant isolation and least privilege
  • Input/output validation and policy enforcement
  • Confirmations, limits and safe fallbacks
03

Launch and oversight

The deployment must be measurable and stoppable.

  • Quality and security evaluations
  • Logs, cost, alerts and audit trail
  • Incident runbook and change lifecycle
03 / DELIVERY

From idea to production

  1. BR / 01

    Discovery

    We select a measurable process and define data, users and unacceptable outcomes.

  2. BR / 02

    Bounded prototype

    A small flow is built with access control, test data and full observability.

  3. BR / 03

    Evaluation and red team

    Normal tasks, failures, abuse and integration faults are tested before production access.

  4. BR / 04

    Controlled rollout

    Deployment is staged with limits, ownership, monitoring and a rapid shutdown path.

04 / EVIDENCE STANDARD

A knowledge assistant without cross-team access

ARCHITECTURE MODEL / NO CLIENT DATA

Security comes from permission filtering before retrieval and before context reaches the model.

This is an architecture pattern, not a client system.

E-1

The user maps to a trusted application identity

E-2

Retrieval filters tenant, owner and data class

E-3

The model has no tool that bypasses authorisation

E-4

Regression tests use documents owned by two isolated teams

05 / OUTPUT

Implementation outcome

01
Architecture decision and risk record
Included deliverable
02
Working, deliberately bounded AI flow
Included deliverable
03
Permission model, data classification and policy
Included deliverable
04
Evaluation set and release criteria
Included deliverable
05
Monitoring, runbook and development roadmap
Included deliverable
06 / QUESTIONS

Common questions

← All services
01Do you implement any AI automation?+

No. We first verify measurable value and whether a model is the right tool. High-risk decisions are not automated without proportionate controls.

02Local model or provider API?+

The answer depends on data, scale, cost and requirements. A reputable API can be operationally safer; self-hosting adds control but transfers responsibility for the entire stack.

03Can you only review an existing project?+

Yes. We can act as an independent architecture reviewer, run threat modelling or test before launch without owning implementation.

BR / NEXT STEP

Have a process you want to automate safely?

We will start with data, permissions and a measurable objective before selecting the model and architecture.

Discuss an AI implementation
NDA · clear scope · direct communication