Control and assurance · International teams
AI governance consulting
Turn AI policy into clear ownership, practical operating rules and evidence your team can maintain.
Understand the service
What this means in practice
AI governance connects the decisions made by leaders with the way staff select, use and operate AI systems. It includes ownership, permitted use, change control, review and incident handling. A policy document is useful only if people can apply it to the tools and workflows they actually use.
Temrik can scope operational governance support around a small use-case inventory. The work can identify who approves a new tool, who owns its data, who checks performance and who can stop it. Legal interpretation, certification and independent assurance require the appropriate specialists; a governance engagement should be explicit about that boundary.
- NIST: AI Risk Management Framework A voluntary framework for managing AI risk.
- Australian government: AI adoption guidance Practical guidance on responsible AI adoption, accountability and testing.
A practical workflow example
A business has several teams using different AI tools. A proposed register records each use case, data category, accountable owner, provider and review date. A new document assistant cannot move from experimentation to customer work until its owner has completed the agreed access and acceptance checks.
Proposed engagement
How we would approach the work
Make usage visible
Inventory the AI-assisted tasks and the people accountable for them. Separate approved use, experiments and unsupported assumptions about tool availability.
Set proportionate rules
Define approval paths according to data and consequence. Translate policy into short operating instructions that staff can follow while doing the work.
Keep evidence current
Agree review dates, change triggers and incident ownership. Use a manageable evidence register so checks remain useful after the initial project.
Deliverables to agree in the scope
- A scoped AI use-case and ownership register.
- A practical approval and change-control process.
- An evidence checklist and recurring review responsibilities.
Access, sample information and reviewer availability affect the plan. Any implementation, provider costs, support arrangements and acceptance criteria are agreed before work begins.
Limits worth understanding
- A framework reference is not a certification or a legal compliance finding.
- Governance requires ongoing ownership; a one-off policy cannot monitor changing providers or use cases.
Questions to bring to the first conversation
- Who may approve a new AI use case?
- What evidence is needed before a pilot becomes routine work?
- Who owns the decision to pause a system?
International teams
Scope the work for your operating context.
For multi-country organisations, define which decisions belong to a central team and which require local approval. Keep entity-specific obligations and provider arrangements visible rather than relying on one universal policy statement.
A starting reference for your review: NIST: AI Risk Management Framework. Local obligations and deployment settings need to be assessed for the actual use case.
A focused next step
Work with Temrik.
Tell us about the workflow you want to improve and the outcome you need. We can review the context and discuss a focused assessment. Scope and price are agreed before paid work begins.