Thrivaca Reduces Unnecessary Cyber Spend by 32%

• Risk expressed in technical terms, not financial terms – Security teams typically report vulnerability counts, patch gaps, or control states. Those technical metrics don’t translate directly into budgets or balance sheet exposure, so finance and procurement cannot compare cybersecurity proposals on an economic basis.
• Data fragmentation across tooling and silos – Asset inventories, vulnerability scanners, incident logs, insurance schedules and business importance tags live in separate systems without reliable joins, making it impossible to build a single exposure dataset for loss modelling.
• Inconsistent data quality and identifiers – Missing timestamps, different hostname conventions, and duplicate or incomplete loss records force heavy preprocessing and reduce confidence in any downstream quantitative model.
• No canonical loss history for frequency/severity estimation – Organizations rarely keep structured records of incident costs (remediation, downtime, legal, reputational) in a way that can feed actuarial style models, leaving estimates to rely on high variance proxies or external averages.
• Manual, ad hoc scenario analysis that isn’t repeatable – “What if” exercises are performed in spreadsheets, depend on one off assumptions, and are hard to audit or reproduce so executives lack defensible comparisons when choosing control portfolios.
• Security tooling often lacks standardized, reliable telemetry – Many security products emit noisy signals or lack programmatic export formats; integrating and normalizing those feeds into a modelling ready dataset is expensive and error prone.
• Models lack actuarial rigor and explainability – When organizations do produce numerical risk scores, they tend to use heuristic or proprietary formulas that are neither transparent to finance nor robust enough for underwriting or board level scrutiny.
• Procurement and finance use different evaluation criteria – Procurement prioritizes vendor risk, cost and SLAs; finance evaluates return on capital. Without a shared monetary metric, control proposals fail to compete on a level playing field.
• Siloed team structures and missing cross functional incentives – Security, finance, and procurement operate with different KPIs and governance rhythms, which prevents rapid alignment on prioritization based on marginal risk reduction per dollar.
• Limited capacity to map controls to marginal risk reduction – Organizations lack empirical, defensible mappings that say “spending $X on control Y reduces expected loss by $Z,” so investments default to heuristics or vendor recommendations rather than optimized portfolios.

These gaps limited operational intelligence and slowed the adoption of a scalable, AI powered product development framework for cyber risk management.

• Consolidated data foundation – Thrivaca connects to CMDBs, vulnerability scanners, EDR, SIEM, and external threat feeds and normalizes the inputs into a single exposure dataset for modelling.
• Actuarial grade loss modelling – Using methods familiar to insurers frequency/severity models and Monte Carlo simulations the platform produces loss distributions and expected annual loss (EAL) estimates with explainable assumptions.
• Control effectiveness & spend optimization – Controls are modelled as probabilistic modifiers so teams can compare “dollars in controls” versus “dollars saved” in expected loss reduction and prioritize high return measures.
• Underwriting & portfolio analytics – For insurers, Thrivaca aggregates exposure across portfolios, computes tail risk metrics and PMLs, and highlights concentration risk enabling more accurate pricing and capital decisions.
• Interactive executive dashboards – Executives and boards receive concise financial summaries, confidence bands, and recommended control portfolios dashboards are built for decision making, not only technical drill downs.
• Explainability and auditability – Every estimate links to source data, model assumptions and scenario settings so auditors and actuaries can validate the outputs.
• Operational use cases– Typical workflows include budget prioritization, pre buy insurance evaluation, underwriting submissions, and regulatory reporting readiness. 

• Risk quantification accelerated by 75% – What previously took multi week exercises now produces defensible EAL figures in hours to a day dramatically increasing decision speed for boards and underwriters.
• Avoidable cybersecurity spends trimmed by 32% – Organizations replaced ad hoc control rollouts with prioritized investments achieving meaningful cost savings by eliminating low impact or redundant controls.
• Underwriting precision improved 20% – Insurer pilots reported better match between premium and exposure, lowering the frequency of under or over insurance outcomes in tested portfolios.
• Compliance and board engagement rose – Converting risk into monetary terms made cyber issues accessible to finance and the board; decision cycles shortened, and recommendations gained traction.
• Fewer compliance gaps and clearer reporting – The platform’s automated mapping to standards (NIST, ISO, MITRE) and audit trails tightened compliance posture and made reporting easier for internal and external stakeholders.