Executive AI Risk Calculator
Turn technical AI exposure into financial impact, board-level KPIs, and an investment case for reducing risk.
Interactive scenario model
Adjust AI adoption, autonomy, data sensitivity, operational impact, and control maturity to model financial exposure and risk-reduction economics.
Reading your result
The number moves as you change the inputs, but on its own it does not tell you what to do on Monday. Here is how we read each band when we walk through a result with a leadership team.
Your AI footprint is still small, or the controls around it are already doing most of the work. Failures are possible, but they stay local and you can recover from them without calling anyone at midnight.
Keep the inventory current and re-run this before the next big rollout. Teams in this band rarely stay here on purpose. They drift upward quietly as adoption spreads team by team.
AI is doing real work in places that matter, and your controls are partly keeping up. In our experience the gap here is almost never policy. It is testing and monitoring.
Pick the two or three use cases carrying the most revenue or regulatory weight and get evidence they behave under pressure. A written policy nobody tests will not hold at this level.
Exposure is moving faster than control. Usually one input has run ahead of the governance around it, and it is either agent autonomy or the sensitivity of the data being handled.
Put approval gates back on the highest impact actions while you build out testing and monitoring. This is the band where one bad afternoon stops being a support ticket and becomes a board conversation.
A material failure is a question of timing rather than chance. Sensitive data, autonomous action, and thin controls are all sitting in the same place at the same time.
Slow autonomy down deliberately instead of waiting for an incident to do it for you, and fund control maturity as a program with a named owner. Anything softer tends to get overtaken by the next deployment.
Bands are a guide, not a verdict. Two companies can land on the same score for completely different reasons, and the fix depends on which input is doing the work. If one slider is clearly driving yours, that is where the conversation should start.
From exposure to economics
Financial value exposed when AI fails, leaks data, makes the wrong decision, or is misused.
Probability-weighted estimate for budgeting, prioritization, and risk acceptance.
Strength of inventory, governance, testing, access, monitoring, and response controls.
Expected financial exposure removed by improving controls and reducing autonomy risk.
Risk dollars reduced for each dollar invested in AI assurance and security.
The formulas behind this calculator are intentionally transparent and illustrative. Replace the assumptions with validated organizational data before using the outputs for formal financial, legal, insurance, or risk-acceptance decisions.
Book a 30-minute executive briefing. We'll pressure-test where AI and security intersect in your business, then point at the wins that are both fastest and safest to take first.
No pitch deck marathon. One focused conversation with the people who'd do the work.