Cyber Risk Quantification Built on Insurance Loss Logic.
Translate cyber exposure into financial terms using a modeled dataset of 320M+ companies, built for underwriting rather than scenario assumptions.
COMPANY RISK REPORT
Illustrative example
Annual Cyber Loss Exposure
$4,280,000
Inherent Risk Score
61/100
Above industry median (54)
Breach Probability
3.12%
Annual likelihood
Incident Cost Range
$2.4M to $7.8M
Single event exposure
Financial Impact by Damage Category
Illustrative exampleWhat is Cyber Risk Quantification?
Cyber Risk Quantification (CRQ) is the process of measuring cyber risk in financial terms, such as expected loss and financial impact by damage category, so that insurers, reinsurers, brokers, and enterprises can make decisions based on dollars rather than abstract scores.
For insurance teams, the value is consistency. A financial output can be compared across submissions, portfolios, industries, and geographies. It also gives brokers and enterprises a common language for discussing risk transfer, coverage adequacy, and remediation priorities.
CRQ is not a replacement for underwriting judgment. It is a structured input that helps reviewers connect technical exposure to likely financial consequences.
Financial Impact by Damage Category
Illustrative exampleHow Cyber Risk Quantification Works
Insurance workflows begin with a company identifier, such as a domain, company name, or portfolio record. The analysis then runs outside-in against Cyberwrite's modeled dataset of 320M+ companies across 56 countries.
The workflow evaluates externally observable indicators and maps them to modeled financial impact logic. The output includes annual cyber loss exposure, financial impact estimates by damage category, benchmarking against industry peers, and risk indicators.
Results are delivered in minutes. Underwriters can use the output during triage, brokers can use it in advisory conversations, and portfolio teams can aggregate it across many companies.
Loss Exceedance Probability Curve
Illustrative exampleWhy Most CRQ Approaches Fall Short
Insurance teams need CRQ that scales across portfolios. Most CRQ tools rely on scenario-based frameworks and manual inputs designed for enterprise security teams. They are difficult to scale across thousands of companies and are not calibrated to insurance loss experience.
Manual workshops can be useful for deep internal risk analysis, but they create friction when a portfolio contains many companies. They also depend on assumptions that may vary by facilitator, participant, and data quality.
For underwriting, the issue is repeatability. A carrier, reinsurer, or broker needs an approach that applies the same logic across every company reviewed.
Data-Driven CRQ vs Scenario-Based Frameworks
Insurance teams compare CRQ approaches by input method, calibration, scale, timing, and primary user. The distinction is operational, not cosmetic.
| Category | Scenario-based frameworks | Data-driven CRQ |
|---|---|---|
| Input method | Manual workshops and questionnaires | Automated outside-in analysis |
| Calibration | Generic scenario assumptions | Insurance loss logic |
| Scale | Single company at a time | Entire portfolios |
| Time to result | Weeks | Minutes |
| Primary user | Enterprise security teams | Insurers, reinsurers, brokers, and enterprises |
The Cyberwrite Approach
Insurance-first quantification starts with modeled company data, financial loss logic, and explainable outputs that underwriting teams can review.
The model is designed to support financial decisions, not only security posture review. It separates damage categories so a reviewer can see which parts of loss are driving the estimate.
Outputs are structured for documentation. The goal is to make each result traceable enough for underwriting, portfolio review, and governance discussion.
Modeled Company Dataset
Data-driven CRQ based on a modeled dataset covering 320M+ companies across 56 countries.
Insurance Loss Logic
Financial impact modeling aligned with cyber insurance loss logic and insurer decision workflows.
Explainable Outputs
Transparent outputs suitable for regulatory review, underwriting scrutiny, and executive reporting.
Outside-In Analysis
Results in minutes with no questionnaires, no installations, and no dependency on manual inputs.
Peer Benchmarking
Illustrative exampleCompany Risk Report Mockup
Insurance teams need CRQ outputs that can be read quickly during submission review. This draft mockup uses illustrative sample values and shows the categories that would appear in a company risk report.
COMPANY RISK REPORT
Illustrative example
Annual cyber loss exposure
$4,280,000
Inherent risk score
61/100
Breach probability
3.12%
Financial impact by damage category
Illustrative exampleUse Cases
Insurance organizations use CRQ to connect single-risk analysis, portfolio action, broker advisory, and vendor exposure into one financial view.
Underwriting teams can use financial impact outputs to support triage and pricing review. Portfolio teams can aggregate outputs to identify segments that require further analysis.
Brokers can use the same financial framing to explain cyber exposure to clients. Enterprises can use it to prioritize controls that are linked to potential loss.
Underwriting
Support risk selection and pricing with financial impact estimates at the company level.
Portfolio Management
Identify accumulation patterns and aggregation risk across books of business.
Broker Advisory
Produce client-facing financial impact reports that explain cyber exposure in business terms.
Third-Party Risk
Quantify vendor exposure and supplier cyber risk in financial terms.
Frequently Asked Questions
Insurance review teams often ask how CRQ differs from traditional scoring, scenario analysis, and manual frameworks. The questions below summarize the operating model, users, outputs, and deployment requirements.
What is cyber risk quantification?
Cyber risk quantification is the process of measuring cyber risk in financial terms, such as expected loss and financial impact by damage category. It helps insurance and risk teams compare cyber exposure using financial outputs rather than abstract scores.
How is Cyberwrite CRQ different from FAIR-based approaches?
Cyberwrite CRQ is data-driven and calibrated to insurance loss logic rather than scenario and assumption driven. The workflow uses outside-in company analysis and modeled loss logic instead of relying only on workshops or manual inputs.
Who uses CRQ?
Insurers, reinsurers, brokers, MGAs, and financial institutions use CRQ to support underwriting, portfolio management, and advisory workflows.
What outputs does CRQ provide?
CRQ provides financial impact estimates by damage type, peer benchmarking, and risk indicators. These outputs are designed to support risk selection, pricing review, and portfolio analysis.
Does CRQ require installing anything?
No. Cyberwrite analysis is outside-in and does not require installing software or collecting questionnaires.