Inventory & classification
Identify AI uses, owners and suppliers, then assess the risks in context.
AI Governance
Give every AI decision a clear owner.
Establish policies, risk classification, approval routes and human oversight across the AI lifecycle, including third-party and generative AI use.
What we address
Governance that reaches the business.
AI governance needs decision rights and operating controls that teams can use. We help turn principles into accountability, oversight and evidence.
Policies & oversight
Define acceptable use, approval requirements and human oversight.
Monitoring & reporting
Set escalation, incident handling, ongoing review and executive reporting.
Engagement outputs
Specific deliverables.
A clear next step.
Scope, evidence requirements and deliverables are agreed before work begins. Each engagement is shaped around your systems, risk exposure and current maturity.
- AI inventory and risk classification
Create a baseline of systems, uses, owners and risk exposure.
- Governance operating model
Establish policy ownership, decision rights and oversight forums.
- Control and oversight requirements
Specify requirements for approvals, monitoring and human intervention.
- Prioritised implementation plan
Map gaps to actions and an evidence-based programme of work.
Common questions
A clear basis for engagement.
Can you work with an existing programme?
Yes. The engagement can review your existing governance arrangements, identify gaps and focus on the controls and operating processes that need improvement.
Which frameworks can inform the assessment?
The assessment can be mapped to agreed frameworks such as NIST AI RMF or ISO/IEC 42001, and to applicable regulatory requirements. Readiness work does not constitute certification or a guarantee of compliance.
Make governance operational.
Discuss your data governance, AI assurance or agentic AI control requirements with Data Angles.