See Data & Artificial Intelligence / AI governance, risk and compliance
Make AI something your board can trust
You need to trust AI to get value from it. Governance is not a brake on AI. It is the set of rules, owners and checks that lets you use it at scale, with confidence that you can explain and defend what it does.
What you can do
Set the rules
Write an AI policy and acceptable-use standard people can follow, and name an owner for every AI system.
Know what you have
Build an inventory of AI systems, models, vendors and data, including the tools staff already use on their own.
Tier the risk
Classify each use case by impact, from low-risk drafting to decisions about people or money, and set controls to match.
Test before and after launch
Check accuracy, bias, security and robustness before go-live, then monitor drift, incidents and cost.
Prove compliance
Map your controls to ISO/IEC 42001, the NIST AI Risk Management Framework, the EU AI Act and regional guidance, and prepare evidence for audit.
What you should know
Regulation reaches further than you think
The EU AI Act applies to organisations that develop or deploy AI affecting people in the EU, wherever they are based. Regional rules are evolving.
Shadow AI is the first risk
Staff are already using AI tools. Without a policy and approved options, sensitive data leaves the company unnoticed.
Standards give you a shared language
ISO/IEC 42001 and the NIST framework turn principles into requirements that auditors recognise.
Human oversight must be real
Approval gates reduce risk only if the reviewer has the time, information and authority to say no.
Governance is continuous
Models, data and rules change. Review cycles, incident procedures and board reporting keep controls alive.
What you will achieve
Defensible AI
Clear records of what AI does, with what data, and why.
Faster approvals
A routine path for low-risk use cases frees teams to move quickly.
Lower regulatory and reputational risk
Controls and evidence ready before a regulator or customer asks.
Board confidence
Simple reporting on AI use, risk and incidents.
Trust from customers and staff
Visible rules that people understand.
A proven path
- 01
Assess
Review data, processes, skills and risk.
- 02
Prioritise
Rank use cases by value, feasibility and risk.
- 03
Prototype
Prove value on real data in weeks.
- 04
Govern
Add controls, testing and human approval before launch.
- 05
Scale
Integrate, train, monitor and extend.
Relevant across every industry
The same discipline applies whatever your sector. Choose yours to see how it plays out.
