Every use case gets a risk pass, before it goes near a customer.
The model is rarely the risk. What it is asked to do is. Risk-Mapped means every AI use case is weighed for what happens if it gets something wrong, not only for what it saves if it gets it right, and that weighing happens before a dollar is spent building it.
What risk mapping actually looks like
Scored both ways
Each proposed use case is scored for downside as well as upside, not chosen on potential savings alone.
Higher stakes, deeper look
Anything touching money, health information or a customer decision gets a more thorough risk pass before it is built.
Part of the free audit
Risk scoring happens inside the free AI audit, so you see it before you commit to anything.
Revisited, not one-off
Risk is reassessed when a system’s scope changes, not scored once at the start and forgotten.
Most AI incidents trace back to a use case nobody actually risk-checked
Data flowing where it should not, or a wrong answer acted on without review, is rarely a surprise in hindsight. It is usually a use case that skipped the risk pass because it looked routine. A short, structured check catches most of it early.
Grounded in
- Australia’s Voluntary AI Safety Standard, guardrail 2 (risk management process)
- NIST AI Risk Management Framework, the Map function
The rest of the framework
All ten guardrails run through every engagement. Here are the other nine.
Frequently asked questions
Isn’t this just for regulated industries?
No, every business handling customer data or making decisions with AI carries some risk. The size of the check should match the size of the risk, not the size of your industry’s regulator.
How long does a risk pass actually take?
For most SME use cases, an hour or two as part of the audit. It is a structured pass, not a compliance department.
Find out where your governance actually stands. Governance is one of six things the free audit scores.
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