AI Boss Tools | Diagnostic 11 • Back to the Hub
Banks have priced operational risk for decades. AI use in most organisations has no equivalent figure, so risk committees argue about exposure without one. DAIVAR gives you the number: a median estimate of the capital exposed to ungoverned AI, calibrated by sector and country from twelve inputs on your size, AI footprint, and controls.
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.
DAIVAR treats AI risk the way banks treat operational risk: it estimates how often a material AI loss event is likely, sizes a typical loss from your revenue, AI usage, and systems in production, then multiplies the two. That figure is scaled by your sector, country, and data sensitivity, modelled as a loss distribution with a bad-year tail, and held to a ceiling of a quarter of your revenue. The base model runs here; the full assessment adds your own loss history and control detail.
Sources: Basel Framework on operational risk (frequency, severity, and control-sensitive scorecards); IBM, Cost of a Data Breach 2025 (sector cost patterns and the added cost of ungoverned AI, used in calibration).
Please enter a valid name and email address.
No spam, ever. Unsubscribe anytime. By continuing you agree to the Privacy & Data Policy.
Calculating…