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Governance designed to work in practice.
Effective AI governance should enable appropriate adoption, not create unnecessary bureaucracy.
Discover → Assess → Control → Assure
DISCOVER — Establish visibility over relevant AI tools, use cases, owners and existing governance arrangements.
ASSESS — Apply proportionate assessment based on use case, data, supplier, impact and organisational context.
CONTROL — Define practical approval routes, responsibilities, mitigations, escalation points and review requirements.
ASSURE — Maintain clear management evidence of decisions, actions, control status and outstanding risks.
A practical route from AI request to governed use
Request → Screen → Assess → Approve / Escalate / Restrict → Apply Controls → Record Evidence → Review
Our principles
Practical — Governance should support good decisions rather than generate documents for their own sake.
Proportionate — Not every AI use requires the same level of scrutiny.
Clear — Management should understand material risks, ownership and outstanding actions.
Evidence-led — Important governance decisions should be recorded and capable of being demonstrated.
Built to evolve — Governance should evolve as AI adoption, regulation and organisational needs change.