The Deepest Cyber Risk Dataset Built for Insurance Decisions.
Cyberwrite has built the deepest cyber risk dataset in the insurance market - risk intelligence on 320M+ companies, 15+ years of breach and claims history, and financial damages models trained on real outcomes. This is the data that powers every decision our platform delivers.
In cyber insurance, the AI is only as good as the data it's trained on.
Every vendor in this market talks about AI. But AI for cyber insurance is only as reliable as the data it was trained on. Most cyber insurance AI lacks the historical depth, coverage breadth, and claims-grounded validation needed to produce decisions underwriters can trust.
Cyberwrite's dataset is different in kind - not just in size. 15+ years of breach and incident history. Risk intelligence on 320M+ companies including the SMB segment. Financial damages models calibrated against real claims outcomes across eight coverage types. Our AI - 4SEEN® and Cyberwrite AI - learns from data with the depth and validation that insurance decisions demand.
The result is a structural advantage. 15+ years of curated breach and incident data. Coverage extending to 99.97% of global businesses. Financial damages models grounded in actual claims outcomes. This is the foundation that makes our AI decisions genuinely reliable - not just fast.
Our six proprietary data assets.
Real-Time Company Intelligence
320M+ companies | Continuous scanning
What We Collect
- Full external technology stack
- Cloud providers and SaaS platforms in use
- Open ports and exposed services
- SSL/TLS configuration and certificate status
- DNS infrastructure and email security (SPF, DKIM, DMARC)
- Subdomain enumeration (150+ subdomains for a typical enterprise)
- Web server and application framework identification
- CDN and hosting provider detection
- Dark web employee passwords
- Malware installed on servers
- 3rd Party SPOF data
How It's Used
This data feeds directly into 4SEEN® risk scoring and Cyberwrite AI underwriting recommendations. It also drives our catastrophe model's dependency mapping.
Why It's Different
Coverage extends to 99.97% of companies with a registered web presence globally - including the SMB segment that enterprise-focused platforms consistently underserve. Risk signals are continuously refreshed, ensuring assessments reflect current posture, not stale snapshots.
Proprietary Breach & Incident Database
15+ years of curated records
What We Collect
- Insurance claims data
- Public breach disclosures
- Dark web intelligence
- Regulatory filings
- Industry partnerships
How It's Used
This is the training foundation for 4SEEN®. When 4SEEN® says a company has a 6.70% probability of suffering a cyber incident within 12 months, that prediction comes from this database.
Why It's Different
What makes this database distinctive is depth, history, and claims-grounded validation. 15+ years of curated records means our models have seen full cyber loss cycles - not just recent trends. Predictions from 4SEEN® are validated against actual insurance outcomes, giving underwriters confidence that probability estimates reflect real-world loss experience.
Financial Damages Models
8 coverage types | Claims-outcome trained
What We Collect
- Fraud & Financial Theft
- Cyber Extortion / Ransomware
- Data Breach / Privacy Liability
- Business Interruption
- Incident Response Costs
- Regulatory & Legal Defense
- Privacy Violation Fines
- Third-Party Liability
How It's Used
When an underwriter sees "$96.5M total estimated loss / $59.1M probable loss," those numbers are generated by our proprietary models trained on actual claims outcomes, adjusted for the specific company's size, industry, geography, technology profile, and risk indicators.
Why It's Different
Financial loss estimates are grounded in real claims outcomes - not industry loss tables or assumed severity distributions. When a report shows $96.5M total estimated loss, that figure reflects modeled outcomes calibrated against actual claims data across all eight coverage types.
Credential Intelligence
Dark web & breach monitoring
What We Collect
- Dark web monitoring for exposed credentials
- Breach database cross-referencing
- Credential severity assessment
- Leading indicator of breach probability
How It's Used
Exposed credential count and severity is a leading indicator of breach probability. Our models have validated the correlation between credential exposure and subsequent claims.
Why It's Different
Exposed credential count and severity feeds directly into inherent risk scoring and Cyberwrite AI's risk factor analysis. The correlation between credential exposure and subsequent claims has been validated against real insurance outcomes - not theoretical correlations.
CYBERPROFILE® Risk Signals
Patented aggregation system | 99.97% global coverage
What We Collect
- Technology exposure cross-referencing
- Credential leakage correlation
- Vulnerability severity mapping
- Peer benchmark analysis
- Historical claim pattern integration
How It's Used
CYBERPROFILE® is the integration layer that turns raw data into structured risk intelligence. It is the input layer for both 4SEEN® AI analytics and Cyberwrite AI decision intelligence.
Why It's Different
The patented CYBERPROFILE® system aggregates signals across all data dimensions into a unified risk profile covering 99.97% of companies globally. Risk scores are grounded in real insurance outcomes - not generic cybersecurity metrics designed for IT security teams.
Technology Dependency Maps
Actual cloud, SaaS, and vendor dependencies
What We Collect
- Cloud provider detection (AWS, Azure, GCP, etc.)
- SaaS platform usage identification
- Technology vendor dependency chains
- First and last detection dates
- Portfolio-wide concentration analysis
How It's Used
This data directly powers our catastrophe model. When we simulate a cloud provider outage, we propagate losses through actual dependency chains - because we know which companies use which providers.
Why It's Different
This granularity is what enables company-level loss resolution in our catastrophe model - not portfolio-level aggregates. When we simulate a cloud provider outage, losses are propagated through actual, mapped dependency chains rather than assumed industry averages.
From our data. Through our AI. Into your decisions.
Cyberwrite's scanning infrastructure collects real-time data on 320M+ companies
Data is enriched with breach database records, credential intelligence, and technology dependency maps
CYBERPROFILE® aggregates signals into structured risk profiles
4SEEN® AI generates risk scores, financial impact estimates, and peer benchmarks
Cyberwrite AI synthesizes everything into specific underwriting decisions, portfolio actions, and executive narratives
Results delivered in seconds via portal, API, or branded reports
The key insight: Our data, models, and AI operate as one integrated system - purpose-built for insurance. Every decision delivered is grounded in real claims outcomes, not generic cybersecurity benchmarks. That is what makes results in under 60 seconds genuinely actionable.