Govern AI
that can act.
AI is moving from generating outputs to executing decisions. It approves requests, escalates incidents, provisions resources, and initiates transactions. Most organisations deploying it have policy, documentation, and oversight committees, but few have the technical controls that can stop a harmful decision in flight. Aivance designs the enforcement layer that puts authority where the action happens, generates the evidence to prove it, and keeps a human accountable.
Free: 30-Minute Enforcement Gap Diagnosis
In 30 minutes, we identify the one missing AI governance control most likely to cost your organisation in the next 12 months, whether regulatorily, contractually, or reputationally.
Within 48 hours, you receive a one-page diagnosis on Aivance letterhead, ready to share with your CIO, CRO, or audit committee.
Book your diagnosis
Why now
A system that only generates output can be reviewed before anyone acts on it. A system that executes does not wait. The governance question changes with it.
Yesterday
AI generated content
Output went to a person. A human read it, judged it, and decided whether to act. The model suggested. The organisation still decided.
Today
As AI gains the authority to act, governance becomes an execution problem. The control has to live where the action happens, not in the document that describes it.
01
POLICY LAYER
Documentation, oversight committees, regulatory frameworks
02
PROCESS LAYER
Approval workflows, post-hoc audits, monitoring dashboards
WHERE MOST GOVERNANCE PROGRAMMES STOP
03
ENFORCEMENT LAYER
Technical controls, deterministic control points, runtime guardrails
RUNTIME OUTCOMES
APPROVED
Executes within authority
BLOCKED
Prevented by control
ESCALATED
Held for human approval
Deloitte State of AI in the Enterprise, January 2026
The governance gap
What most organisations already have
What few have
Governance breaks down when systems can act but controls cannot intervene before actions occur.
Who engages Aivance
Governance work is rarely proactive. Something changes, and the question becomes urgent. These are the situations Aivance is built for.
Technology and SaaS
AI pilots are working. Scaling them to production has stalled because governance was never designed in.
Enterprise customers, particularly in regulated sectors, are asking about AI governance before signing contracts. The product works. The governance posture that would let a large customer approve it does not exist yet. That is the gap this work closes.
Professional Services
AI tools are embedded in client-facing work. An enterprise client is asking how they are governed before the contract renews.
Law firms, accounting practices, and consulting firms are using AI across document review, research, and delivery. An enterprise client, an upcoming audit, or a referral partner has asked how those tools are governed and what controls exist. The answer needs to be defensible, not a list of tools with access permissions attached.
Mid-Market Businesses
The board has asked its first AI governance question. No one in the room had a confident answer.
AI tools are live in operations, finance, or customer-facing functions. A board member, an investor in a due diligence process, or an incoming enterprise customer has asked whether the business can demonstrate that its AI systems operate within defined boundaries. That question now needs a real answer.
How we help
Each addresses a different starting point: the audit, the stalled pilot, or the override question from a regulator or board. Each produces specific, auditable outputs.
Diagnoses enforcement gaps across IMDA, MAS AIRG, PDPA, ISO 42001, and the EU AI Act, separating controls that exist on paper from controls that are technically real.
Diagnoses why each AI pilot stalled and designs the governance scaffolding that gets it to production.
Designs who holds the kill switch and what happens when they use it, including the Suspended Handoff State that halts an agent until a human ratifies.
Featured insights
Singapore's updated Model AI Governance Framework includes over ten case studies. One of them, Terminal 3's payroll agent, shows what enforcement-layer governance actually requires technically. Here's what separates it from the others.
Read article →Most AI governance failures are not systems that misbehave. They are systems that proceed. The model produces output, the pipeline accepts it, the system executes, and no one ever explicitly granted that authority. Here is what governance looks like when it is designed rather than assumed.
Read article →Start with the free 30-Minute Enforcement Gap Diagnosis. In 30 minutes, we identify the one missing AI governance control most likely to cost your organisation in the next 12 months. Within 48 hours, you receive a one-page diagnosis on Aivance letterhead.
Book Your Enforcement Gap Diagnosis