AI Boss Tools | Diagnostic 11 • Back to the Hub
How much capital does your organisation have exposed to ungoverned AI use? DAIVAR estimates it as a median figure, calibrated by sector and country, from twelve inputs about your size, your AI footprint, and your controls.
Banks have measured operational risk for decades, tracking how often losses happen, how large they are, and how controls change both. AI use in most organisations has no equivalent measurement. Tools get adopted and systems go into production without anyone estimating what a bad year of AI-related losses would cost.
DAIVAR applies that operational-risk thinking to AI. It combines quantitative inputs (revenue, headcount, AI usage, systems in production) with a qualitative read of your controls, then calibrates by sector and country to produce your Exposed Capital at Risk: a median estimate, the estimated annual likelihood of a material AI loss event, and what drives your number.
Use the estimate the way risk committees use any VaR-style figure: to size the problem, justify the control budget, and compare yourself against a typical organisation of your profile.
Calculate My DAIVAR →DAIVAR treats AI risk the way banks treat operational risk. It estimates how often a material AI loss event is likely to happen, and how large that loss would be, then multiplies the two. Here is the shape of it, without the internal weightings.
We start from a baseline annual likelihood of a material AI loss event, then move it up or down based on how weak your controls are (policy, oversight, training, detection), how intensively your people use AI, and whether you have had incidents before.
We size a typical loss from three things: a share of your revenue, the number of employees actually using AI, and the AI systems you run in production. That figure is then scaled by your sector, your country, and how sensitive your data is.
Frequency times severity gives your median estimate, the figure you see. We also model a bad year as a high-percentile tail, and hold every estimate to a hard ceiling so it never exceeds a quarter of your revenue.
The numbers are calibrated with published breach-cost research and Basel operational-risk practice, and your result is compared against a typical organisation of your size and location so you can see where you stand.
The banking standard for measuring operational risk. DAIVAR works in this tradition: frequency, severity, and control-sensitive scorecards.
Published breach-cost research used in calibration, including sector cost patterns and the added cost of ungoverned AI.
This calculator runs the base version of DAIVAR: a simplified estimate you can use to size the problem. The full assessment adds your own loss history and control detail.
Your median Exposed Capital at Risk estimate, the likelihood of a material AI loss event, what drives your number, a median benchmark for your profile, and a downloadable one-pager report. Free once you enter your name and email.
The twelve inputs take about two minutes, and rough figures are fine because the model is calibrated to work with estimates. Your business figures are not stored; only your name, email, and result tier are recorded when you unlock the report.
Please enter your revenue, employees, AI usage percentage, and systems count.
This is the base version of DAIVAR. It returns an indicative planning estimate to help you size the exposure, not a final figure. The full assessment goes deeper on your sector, your controls, and your own loss history.
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