A fractional security function for companies not yet ready for a full-time CISO. Over a focused 2–3 month engagement, we quantify your exposure, show you exactly what reduces it, and hand you a roadmap sized to your stage.
What this is — and what you get
We model cyber risk the way finance and insurance model uncertainty — in probabilities and dollars. You see how much each control actually reduces your exposure, so every dollar you spend has a measurable return. No long-term contract. No over-engineering. Just enough structure to keep moving fast while staying credible with customers and investors.
This sample uses two recognised baselines — OWASP Top 10 and CIS Top 18 — chosen because they represent the common risks every cloud-native business shares. Figures are illustrative for a model client and benchmarked to the ASD ACSC Annual Cyber Threat Report 2024–25.
Executive summary
This model organisation shows the risk profile we typically see in early-stage, cloud-native startups: a thin control environment carrying more exposure than the business realises. These are the numbers as found — before any remediation.
In context — ASD ACSC 2024–25 average cost of a single incident
Our expected annual loss sits above the single-incident benchmark because it aggregates a full portfolio of risks across a year — not one incident.
Where the exposure comes from
Weak authentication and access control.
Production gaps from speed-to-market trade-offs.
Unmanaged dependencies.
The bottom line for the board
Residual risk sits above appetite at every loss threshold tested. The business is currently carrying more cyber exposure than its size and revenue can comfortably absorb. This is a management decision, not a technical one: either lift the control environment, or formally accept the risk.
The evidence
Infrastructure gap analysis — findings by severity
We scanned the production environment against best-practice baselines. The pattern is consistent with early-stage startups: production was reached at speed, and security baselines lagged behind delivery. None of it is unusual — and all of it is fixable inside normal delivery cycles.
Control coverage by framework
| Framework | Failed | Passed |
|---|
Our recommendation
Fold remediation into the existing DevOps backlog and fix in the lowest environment first, then promote up. Add continuous validation in staging so new risks are caught before they reach production.
Cyber risk register — inherent likelihood × impact
Each risk is modelled with a Bow-Tie structure that maps causes, controls and consequences — so the register is decision-ready, not a static list. Click a cell to see the risks sitting at that likelihood and impact.
Click any cell in the matrix to list the risks it contains. Darker cells carry more combined likelihood and impact.
quantsRisk — exposure you can act on
We take the risk register and run 5 million Monte Carlo simulations to derive residual risk — the loss profile that remains after your controls. The panel below runs a live taste of that same engine. Toggle the remediations we’d sequence into your sprints and the entire loss curve shifts left — proof that every dollar of security spend has a measurable return.
Each is a control we’d fold into your DevOps backlog. Toggle to model its effect on residual exposure.
Modelled loss by risk — where the exposure concentrates
Dashed markers show the current-state contribution. As controls come on, the bars shrink — the largest reductions come from the risks driving the most loss.
Recommendation & next steps
There are fundamental, fixable cyber risks across this organisation, and the infrastructure is not yet aligned to best practice. This is normal for the stage — and exactly what a time-boxed DSO engagement is built to resolve. Our guiding principle: every dollar you spend on security should measurably reduce your loss exposure — and each iteration of this report tracks that reduction.
Confirm the loss tolerances and assumptions with your leadership team.
Sequence the roadmap against your budget and growth plan.
Remediate in sprints, with us steering and your team delivering.
Re-run the model to prove exposure is falling.
A DSO engagement gives you a board-ready view of your cyber risk in dollars within weeks — not a generic checklist, but your exposure, your priorities, your roadmap.
Assumptions: model org is an Australia-based startup, no current certification, cloud-native on AWS or Azure, <AUD 1M revenue, <20 staff. Loss tolerances are illustrative and set by Secure Measure; in a live engagement they are validated with the client. Bounds use a 90% confidence level. Probability of loss benchmarked to the ASD ACSC Annual Cyber Threat Report 2024–25 and the IBM Cost of a Data Breach Report 2025. Control effectiveness for an early-stage startup assumed at 25–40%.