Governance Controls Build

The situation

You went through a governance assessment, or wrote the policy yourselves, and reached real decisions: which AI tools may touch customer data, who approves a new connection, how long logs are kept. Those decisions live in a document, a slide deck, or the notes from a workshop. Nobody has gone back and built them into the platforms your business actually runs on.

The exposure the assessment found has not moved, because a decision that only exists on paper does not stop anyone from doing anything. Six months from now, the same conversation happens again: the same gaps, the same recommendations, because deciding and installing turned out to be two different pieces of work, and only the first one got done.

What changes

Your decisions stop being a document and start being the way the platform actually behaves. The tool a new hire tries to connect either matches an approved pattern or waits for a real approval. Data classified as sensitive carries sharing restrictions that hold, because the platform enforces them rather than a policy asking politely.

A decision that only lives in a meeting note has not actually been made yet.

The register at the centre of it stays current on its own, updated by the approval workflow every time something changes, so you are never presenting a document that was accurate six weeks ago, and an auditor or an insurer can see exactly when each control last changed.

How it runs

1. 01

   Translate decisions into a control list with owners

2. 02

   Implement in the platforms you already run

3. 03

   Stand up the register and approval workflow

4. 04

   Switch on monitoring and evidence reports

5. 05

   Hand over with a runbook

We start from your decisions, not from a generic control framework, translating each one into a specific control with a named owner and a platform it lives in. Implementation happens inside the tools you already run: your identity provider, your collaboration suite, your data platform, so nothing new appears for your team to learn.

Once the controls exist, we stand up the register and the approval workflow that keeps it accurate, then switch on monitoring for new connections, data reaching AI services, and control drift, with reports landing in your inbox every month. Handover includes a runbook, so the loop keeps running under your own team if that is the direction you choose.

What you receive

You receive the controls themselves, installed and enforcing inside the platforms you already run rather than living in a separate tool your team has to remember to check. Alongside them, you get the register: every AI system in use, its owner, its data and its approval, kept current automatically by the workflow underneath it.

You also receive the approval workflow itself, the monthly monitoring and evidence reports, and a runbook that hands the whole loop to your team with nothing left implicit. Afterwards someone has to keep the loop running: a named owner inside your team, or a standing seat we provide.