The gap between governance policy and governance enforcement is where your liability lives.

The problem this solves

There are two distinct governance problems that most organisations conflate. The first is the policy problem: do you have written governance documentation that maps to the applicable frameworks? The second is the enforcement problem: are those governance commitments technically real, or do they depend entirely on people following procedures they might not follow under pressure?

Most audits only test the first problem. They check whether you have policies and whether they reference the right frameworks. That is the easy part. The hard part — and the part where actual liability lives — is whether your systems have enforcement controls that work independently of human diligence. Whether your AI can be halted mid-execution. Whether your human oversight mechanisms provide real information and real authority, or just the appearance of review.

This audit tests both layers. The regulatory landscape it covers — MAS AIRG, IMDA's Model AI Governance Framework, PDPA, ISO/IEC 42001, and the EU AI Act — provides the compliance context. The enforcement layer assessment provides the honest picture of whether your governance is real.

What the assessment covers

The engagement begins with a discovery session to map your current AI systems: what is in production, what is in development, what third-party AI tools are in use, and who is accountable for each.

From there, a structured five-dimension assessment examines:

Risk identification and classification. Each AI system is assessed for the risk it presents, using a framework aligned to IMDA and ISO 42001 classifications. High-risk systems get more scrutiny.

Regulatory gap analysis. Your current governance posture is mapped against each applicable framework. Not a generic checklist — a specific assessment of where you stand against the rules that apply to your business.

Enforcement layer quality. The central question of this audit: are your governance controls technically enforced, or are they procedurally aspirational? This dimension tests whether your human oversight mechanisms — the interfaces, the decision points, the escalation paths — are genuinely capable of stopping a harmful decision in flight. Monitoring what happened after execution is forensics, not governance.

Documentation and operationalisation. Policies that exist but are not followed are not compliance. This dimension checks whether governance exists on paper and in practice.

Monitoring and incident response. Whether you have any mechanism to detect AI system failures, behavioural drift, or unexpected outputs after deployment.

What you receive

At the end of four weeks you receive:

• A written risk assessment covering all AI systems in scope, with a traffic light risk rating for each
• A regulatory gap analysis identifying specific compliance obligations you are not currently meeting
• A prioritised remediation roadmap with recommended actions ranked by risk and effort
• A board-ready executive summary, three pages maximum, that a director can read and understand without a technical background

How it works

The engagement requires approximately 20 hours of your team's time over four weeks. This includes a half-day discovery session, two structured review sessions, and a closing presentation. The rest of the work is done by Aivance.

This is not a tick-box exercise. Every finding is specific to your systems, your stack, and your regulatory context. The audit distinguishes between controls that are technically enforced and controls that are aspirationally documented — and that distinction is what determines whether your governance would hold up in a real incident, a regulatory inquiry, or a board examination.