A quick, directional sense of the exposure you're carrying by running AI without governance — so you can decide how urgent this is.
Five inputs. You'll get a directional figure and the main drivers behind it.
Estimated annual exposure from ungoverned AI
How this figure is calculated — and the basis for it
Expected annual exposure = likelihood of an AI-related incident × the typical cost of a business incident. Likelihood starts near 17%/year — about 40% of small businesses face a cyberattack each year, and 43% of breaches now involve “shadow AI” — more than double a year ago (40% × 43% ≈ 17%). It rises with ungoverned tools and no policy, and falls when governance is in place. Cost per incident is placed inside Verizon's documented typical-business range of $120,000–$1.24M, scaled by your headcount, data sensitivity, and regulation. The exact likelihood and incident cost used for your number are shown in the line above.
Sources: Verizon 2026 Data Breach Investigations Report — business attack exposure and typical incident range · IBM Cost of a Data Breach 2026 — 43% of breaches involve shadow AI (more than double the prior year), close to 70% of breached organizations lack AI governance, AI-driven attacks cost ≈ $1M more, and the global average breach reached $4.99M.
Note on bias: IBM and most breach-cost publishers are security vendors with an interest in larger figures — so this model deliberately anchors on Verizon's lower, incident-level business range and a conservative likelihood, and ignores IBM's much larger $4.99M average. It aims to under- rather than over-state.
A 45-minute session to pressure-test these numbers against your actual operations and prioritize where to start.
Directional estimate for prioritization, not a formal risk quantification. Real exposure depends on facts unique to your business.
This site uses privacy-friendly Google Analytics to understand anonymous traffic and tool usage. No personal data, and nothing you type into the tools is tracked. See our privacy note.