Quantitative Risk
Most organisations have a good sense of where they are weak, but far less clarity on which weaknesses matter most in financial terms.
Spartacus Quantitative Risk helps turn cyber risk into clearer financial insight, giving organisations a more practical basis for prioritisation, investment, and risk-informed decision-making.

Is this Cyber Risk Scenario material enough to act on?
Quantitative Risk helps organisations decide where financial exposure matters most, which scenarios deserve closer attention, and where protection effort is most justified, while giving teams a more practical way to compare scenarios, challenge assumptions, and support decisions that need to stand up in executive, risk, or assurance discussions.
Qualitative risk views can be useful for discussion, but they often stop short of showing what exposure actually means in financial terms. Without that context, it becomes harder to judge materiality, compare scenarios, and decide where protection effort should be focused first.
Spartacus Quantitative Risk applies recognised quantitative risk principles in a governed way, ensuring outputs remain clear, consistent, and usable.
Quantitative Risk works alongside existing tools like maturity assessments and supplier assurance rather than replacing them.
It adds a financial layer to cybersecurity decisions, helping to justify investments and prioritize programme oversight.
Organisations can connect financial exposure to their overall security posture without needing to switch to a new way of working.
Spartacus supports two quantitative risk approaches so organisations and consultancies can choose the level of insight and analytical depth they need.

A practical starting point for organisations that need clearer financial context without the depth of advanced modelling. Standard Risk applies a FAIR-aligned model in a bounded, deterministic way, helping teams estimate exposure, compare scenarios, and support prioritisation and investment decisions.

A deeper option for organisations that need more advanced financial analysis under uncertainty. Comprehensive Risk applies a FAIR-aligned model with Monte Carlo simulation to provide a richer view of likely exposure, variability, and decision-grade risk insight for more complex scenarios.