CISOs are fluent in MITRE ATLAS techniques. Boards are fluent in dollars. The translation between those two languages is where most AI security budget requests stall. This dashboard does the translation — modelling the financial exposure of specific AI incident scenarios based on your actual data, systems, and revenue.
The AI Incident Cost Dashboard™ converts technical AI security risk into financial exposure estimates. Enter your industry, company size, revenue, customer count, the types of sensitive data you process, and the AI systems you operate (agents, RAG, copilots). The dashboard models the cost of specific incident scenarios — data breach, regulatory fine, agent compromise, reputational damage — using a cost waterfall that breaks down exactly where the financial exposure comes from.
"We have a critical gap in agent security coverage" is a sentence every CISO has said in a board meeting, with limited effect. "An agent compromise incident at our current customer and data volume would cost an estimated $2.1M in breach notification, regulatory exposure, and remediation" is a sentence that gets budget approved. The Incident Cost Dashboard exists to produce the second sentence from your organisation's actual numbers, not a generic industry estimate.
The dashboard breaks incident cost into discrete categories that sum to a total exposure figure: breach notification costs (scaled to your customer count and applicable regulations), regulatory fine exposure (based on your industry's specific frameworks — GDPR, HIPAA, state breach laws), incident response and forensics, customer churn and reputational impact (modelled against your revenue and industry churn sensitivity), legal costs, and remediation/system hardening costs post-incident.
Each scenario — data leakage, agent compromise, model theft, RAG poisoning, supply chain compromise — gets its own cost waterfall, so you can see not just the total but exactly which cost categories drive the largest exposure for each specific threat type.
The same security gap produces wildly different financial exposure depending on what data is at risk and how many customers are affected. A prompt injection vulnerability in a system processing public marketing content carries minimal financial exposure. The same vulnerability in a system processing healthcare records or financial data, serving millions of customers, carries exposure in the tens of millions. The dashboard's multiplier model accounts for this — toggle the data types you process (PII, PHI, financial data, IP) and the AI systems you operate, and the cost model adjusts accordingly.
The pie chart and comparison view let you see relative exposure across incident types side by side. If agent compromise carries 3x the financial exposure of a standard data leakage incident in your specific risk profile, that's a data-driven argument for prioritising agent security investment over general data loss prevention — exactly the kind of comparison a CFO or risk committee wants to see before approving budget allocation.
Enter your organisation's profile and see the dollar cost of specific AI security incident scenarios. Board-ready PDF included.