Governance of AI

How do we keep AI lawful, safe, accountable and fit for purpose?

The management system around AI: decision rights, policies, controls and evidence spanning discovery, procurement, development, deployment, operation and retirement.

Operating principles

What good
looks like.

Accountability

A named person remains answerable for the use case and its outcomes.

Proportionality

Controls increase with potential harm, autonomy, scale and uncertainty.

Lifecycle control

Approval is a continuing obligation, not a one-time gate.

Evidence

Claims about value, safety and compliance are supported by reviewable evidence.

Put it into practice

Make it
operational.

Establish the system

Define policy, scope, committees, delegated authority and interfaces with existing governance.

Register every use case

Record purpose, owners, users, data, suppliers, models, dependencies and affected groups.

Set decision gates

Require evidence at concept, design, validation, deployment, material change and retirement.

Operate exceptions

Time-limit exceptions, document residual risk and name the authority accepting it.

Minimum evidence set

Make the decision
reviewable.

01AI policy and governance charter

02AI system and use-case register

03Risk-tiering and approval standard

04Decision, exception and change records

Move from knowledge to practice

Design governance teams can operate.

Connect accountability, evidence and oversight to the way AI is actually delivered and used.

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