OPERATING MODEL
Instead of another policy pack, the framework becomes a live operating model: clear decision rights, practical rules, approval gates, control evidence and metrics that leaders can use to steer AI adoption.
Control signal
Good governance makes AI use visible enough to approve, monitor and improve. The page below is structured around that signal: what is being used, who decides, which controls apply and what evidence exists.
A practical AI governance framework needs repeatable movement from discovery to decision to evidence. Each stage creates an artefact the organisation can keep using.
01
Register
Capture AI tools, vendors, data types and use cases before adoption becomes informal.
02
Review
Assess risk, security, privacy, legal and business context with clear escalation rules.
03
Approve
Define accountable owners, guardrails, acceptable use and required controls.
04
Evidence
Track metrics, residual risk and control records for leadership and audit confidence.
Define who evaluates AI use cases, resolves conflicts, approves exceptions and owns ongoing oversight.
RACI
Thresholds
Escalations
Create stage gates for intake, assessment, approval, implementation and monitoring.
Intake
Review
Monitor
Keep acceptable use, approved tools, data handling and development standards simple enough for teams to follow.
Policy
Standards
Translate responsible AI principles into security, privacy, access, logging, vendor and data controls.
Security
Privacy
Vendor
Retain proof that AI decisions, exceptions, control checks and outcomes are visible to leadership.
Metrics
Reports
Records
Practical outcome
Rules, owners and evidence in one framework.
Practical AI governance connects decision rights, ethical principles, policies, lifecycle controls and measurable oversight.
Rules
Owners
Controls
Evidence
Overview
What becomes clearer
Who approves use cases, what data is allowed, and which controls apply by risk tier.
What happens next
A practical framework, policy set and evidence trail that teams can use after the workshop.
GOVERNANCE CADENCE
The framework should not stop at launch. We help teams establish a repeatable cadence so AI tools, use cases, controls and exceptions are kept current as the organisation learns.
Register AI tools, vendors, data types and business owners before adoption becomes informal.
Triage security, privacy, legal and business risk with clear approval thresholds.
Apply baseline controls for access, data handling, vendor use, monitoring and exceptions.
Review metrics, incidents, benefits and residual risk so governance matures with adoption.
The outcomes
AI steering committee roles and decision rights
Acceptable use rules and approved tool guidance
Lifecycle stage gates for AI features, vendors and use cases
Baseline controls linked to data, access, privacy and security risk
Metrics and evidence for oversight, reporting and future ISO 42001 readiness
A framework that connects principles, risks, controls and evidence.
01
Principles
Anchor decisions in responsible AI expectations.
02
Risks
Identify what can go wrong by use case.
03
Controls
Apply baseline and risk-tiered safeguards.
04
Evidence
Record decisions, metrics and assurance outputs.
Frequently asked questions
A few practical points that usually come up before an AI governance engagement.
Book a Readiness Call
Next step
Book a Readiness Call