AI Governance Framework Design
Define the authority, controls and evidence your AI use needs.
Aivance maps who can make each decision, under which conditions and within which execution boundaries. The work links policy to the controls your team must configure, the people accountable for them and the evidence needed to review their operation.
The work this covers
Policy states the decisions people should make. Configured controls determine the actions a system can take. Framework design connects those decisions and actions through defined authority, execution boundaries and evidence.
We begin with your AI use cases, including AI embedded in material vendor services and tools used outside central oversight. The discovery record identifies owners, systems, models and dependencies. Gaps in visibility are documented with their risks and mitigating measures.
The framework defines who approves AI use, who assesses materiality, who can intervene and how exceptions reach the right owner. It records the controls your team operates and the evidence that shows whether they hold.
For Singapore financial institutions in a global group, a group framework must meet the AIRG locally. Local senior management remains accountable to MAS. Aivance checks the group framework against the AIRG and documents local gaps, reporting needs and escalation responsibilities.
Enforcement Layer: configured controls, execution boundaries and intervention.
Evidence: assessment rationale, test results and decision records.
What the framework includes
The engagement produces editable policy, architecture and operating documents for the use cases in scope.
AI discovery and inventory. A linked register of use cases, systems, models, providers, owners and dependencies. It includes vendor-embedded AI, shadow AI findings, discovery limitations and a process for keeping the register current.
Authority Architecture Map A map of who can approve, deny or escalate consequential decisions. Each authority rule is linked to its configured control and retained evidence.
Execution Boundary definitions. The permitted actions, limits and conditions for autonomous operation, with triggers for human review. Where a control is missing, the framework specifies what must be configured or built.
AI governance policy. A written policy covering permitted use, accountabilities, materiality assessment, review and incident handling. The wording is designed for the people who must follow it.
Roles and accountability matrix. Owners, reviewers and escalation paths from the use-case team to senior management and the board. The designated control function retains independent oversight of identification, inventory and materiality decisions.
Governance calendar. Review, testing and reporting cycles with defined owners, inputs, outputs and triggers for earlier review.
Implementation playbook. Instructions for introducing AI use cases, maintaining their records, configuring controls and handling changes or incidents.
Applicability and gap mapping. A record of relevant supervisory expectations, laws and standards. IMDA guidance informs the approach where relevant. Group-framework findings identify the additional work needed in Singapore.
How it works
The six-week engagement includes an inventory and design workshop, structured design sessions, a document review cycle and an implementation briefing. We agree access, owners and evidence requirements before discovery begins.
You receive editable documents and the decisions behind them. Your internal team or selected delivery provider implements the agreed changes, with responsibilities stated in the scope.
A governance framework should identify the owner, control and evidence for each consequential decision. The design gives your team a defined basis for implementation and testing.
Related services
Find out whether your configured agent controls work in practice.
Start with the complimentary 30-minute Authority & Control Review. Within 48 hours, you receive a written scoping note for the relevant governance and risk stakeholders.