Greatest exposure
Portco E · $5.7MThe OT / IT boundary gap carries the largest modeled financial cyber exposure.
Worked example · seeded demo portfolio
This seeded, illustrative portfolio shows comparable financial cyber exposure, a constrained-budget funding order, and the assumptions, evidence lineage, confidence, and freshness behind the recommendation. It is not customer data or a customer outcome.
Request a Proof Pack on your evidencePortfolio funding decision · illustrative
Greatest exposure
Portco E · $5.7MThe OT / IT boundary gap carries the largest modeled financial cyber exposure.
Fund first
Portco C · 19×A $48K MFA action has the highest modeled exposure reduction per dollar.
Budget result
$538K → $6.8MFund C, A, and B to maximize modeled exposure removed inside the $550K cap.
Constrained-budget recommendation
Allocate $538K across three actions; keep $12K unallocated.The highest-exposure company is not automatically the first funded action. The order optimizes modeled exposure removed within the approved cap, then keeps the excluded actions visible for the next decision.
| Order | Company | Exposure | Action cost | Exposure removed | Modeled return | Decision |
|---|---|---|---|---|---|---|
| 01 | Portco C | $1.1M | $48K | $0.9M | 19× | Fund now |
| 02 | Portco A | $2.4M | $180K | $2.2M | 12× | Fund now |
| 03 | Portco B | $4.1M | $310K | $3.7M | 12× | Fund now |
| Next | Portco E | $5.7M | $420K | $5.1M | 12× | Next budget |
| Hold | Portco D | $3.3M | $260K | $2.9M | 11× | Evidence pending |
Why the funding order changes
Exposure concentration alone puts Portco E first.
The selected combination removes more modeled exposure within the $550K cap.
After funded fixes are evidenced, residual exposure and confidence are recomputed.
Portco A drilldown
Open one recommended action and inspect the model, source evidence, confidence, freshness, and claims that remain blocked.
Financial cyber exposure · base
$2.4MP10 $1.6M · Base $2.4M · P90 $3.5M. That is ~5.7% of a $42.0M EBITDA pool.
Top driver
Unpatched external attack surface → ransomware exposure. Remediation $180K → ~$2.2M modeled exposure removed (12× return).
FAIR factors & assumptions (visible, challengeable)
| Factor | Assumption | Source |
|---|---|---|
| Loss event frequency | 0.35 events/yr (ransomware via external surface) | Scanner + threat baseline |
| Primary loss magnitude | $3.1M (IR, downtime, rebuild) | Sector incident data |
| Secondary loss magnitude | $3.4M (regulatory, churn, legal) | Sector incident data |
| Control modifier | MFA 58% → frequency uplift | Okta export (API-verified) |
| Simulation | 10,000 Monte Carlo runs | FAIR engine |
Remediation economics
| Item | Value |
|---|---|
| Remediation cost | $180K |
| Modeled exposure removed (base) | $2.2M |
| Modeled return per dollar | 12× |
| Exposure after fix (base) | $0.2M |
| Confidence band | P10 $1.6M · P90 $3.5M |
What the board sees (illustrative brief excerpt)
“Financial cyber exposure at Portco A is estimated at $2.4M (P10 $1.6M / P90 $3.5M), ~5.7% of the EBITDA pool, driven primarily by an unpatched external attack surface and sub-60% MFA coverage. The recommended fix costs $180K and removes ~$2.2M of modeled exposure, a 12× return. Method: FAIR-aligned Monte Carlo, 10,000 runs; every figure traces to a source system with freshness shown.”
Blocked-claim ledger · travels with the artifact
Seeded, illustrative demo scenario. Not customer data, a customer artifact, or a customer outcome. All figures are decision-support estimates (FAIR-aligned Monte Carlo), not actuarial, legal, or investment advice.
Next step
A design partnership applies this decision flow to approved evidence, reviews the model and gaps with your team, and returns a claim-reviewed Proof Pack for internal use.