Claims-Based Risk
Claims-Based Risk is a risk assessment methodology used primarily in cyber insurance and advanced risk management that estimates the probability and financial impact of a cyber incident from historical loss data and insurance claims.
Unlike vulnerability-based risk, which looks at technical flaws (e.g., "You have an unpatched server"), or threat-based risk, which looks at attacker activity (e.g., "Hackers are targeting this sector"), claims-based risk looks at actuarial reality (e.g., "Companies with this specific configuration have filed ransomware claims averaging $2.5 million").
This approach grounds cybersecurity discussions in financial reality. It moves the conversation from theoretical technical possibilities to proven historical patterns of loss, allowing organizations and insurers to price risk accurately and prioritize defenses that prevent the most expensive, real-world incidents.
The Core Mechanics of Claims-Based Risk
This methodology operates on the principle that the best predictor of future loss is analyzing past losses across similar, distinct peer groups. It relies on three primary data points.
Loss Frequency: How often a specific type of incident (like Business Email Compromise) occurs within a specific industry or revenue band.
Loss Severity: The total financial cost of those incidents, including forensic investigation, legal fees, regulatory fines, and ransom payments.
Correlated Controls: The specific technical conditions (such as the absence of Multi-Factor Authentication or open RDP ports) that were present in the victim organizations at the time of the claim.
How Claims-Based Risk Differs from Technical Risk
Understanding the distinction between technical findings and claims data is critical for modern security leaders.
Technical Risk View: A vulnerability scanner may flag a "Medium Severity" issue with an SSL certificate. Technically, it is a flaw.
Claims-Based Risk View: Actuarial data might show that this specific SSL issue has never resulted in a paid insurance claim, whereas a "Low Severity" misconfiguration in Microsoft 365 has caused $100 million in aggregate losses. The claims-based model would prioritize the Microsoft 365 issue, even if the technical scanner ranks it lower.
Why the Industry is Shifting to Claims-Based Models
The cybersecurity industry is increasingly adopting claims-based logic to address "alert fatigue" and justify security budgets.
Budget Justification: CISOs use claims data to show the Board of Directors the potential cost of inaction. "Peers who ignored this risk incurred an average cost of $4 million" is more persuasive than "We have a critical vulnerability."
Insurance Insurability: Cyber insurers are moving away from long questionnaires. They now scan an applicant's perimeter and apply a claims-based model. If the applicant matches the profile of a "high-claims" company (e.g., open RDP ports), they may be denied coverage regardless of their policy documents.
Legal Defensibility: Using claims data demonstrates a "Standard of Care." It demonstrates that the organization addressed the risks known to cause actual damage in its industry.
The Limitations of Claims-Based Risk
While powerful, this model acts as a "lagging indicator," meaning it looks backward rather than forward.
Novel Threats: Claims-based models cannot predict "Zero-Day" attacks or entirely new attack vectors because there is no historical claims data for them yet.
Data Scarcity: For small industries or new technologies, there may not be enough statistically significant claims data to build an accurate model.
Survivor Bias: It only analyzes reported claims. It does not account for "near misses" or attacks that were successfully repelled, potentially skewing the perception of risk.
Frequently Asked Questions
How does claims-based risk affect cyber insurance premiums? It is the primary driver of pricing. If your organization shares technical characteristics (like open ports or specific software versions) with companies that frequently file claims, your premiums will be significantly higher.
Can claims-based risk help with ransomware protection? Yes. By analyzing thousands of ransomware claims, this model identifies the specific "entry vectors" (like weak RDP credentials or phishing) that actually lead to encryption. Closing these specific gaps significantly reduces the likelihood of a claim.
Is claims-based risk the same as quantitative risk analysis? It is a type of quantitative analysis. While quantitative analysis can use Monte Carlo simulations based on theoretical assumptions, claims-based risk relies solely on empirical loss data from insurance payouts.
Does a clean claims history mean low risk? Not necessarily. In cybersecurity, "past performance is not indicative of future results." A company might have a clean history simply because it hasn't been targeted yet, not because it is secure. Claims-based risk looks at the industry's history, not just the individual company's.
How ThreatNG Mitigates Claims-Based Risk
ThreatNG empowers organizations to manage Claims-Based Risk by identifying and remediating the specific technical indicators that cyber insurers and actuarial models associate with financial loss. In the insurance world, risk is not just about having a vulnerability; it is about having the specific vulnerabilities that historically lead to payouts, such as open Remote Desktop Protocol (RDP) ports or unverified email domains.
ThreatNG serves as a pre-underwriting engine, enabling organizations to view their attack surface through an insurer's lens. By detecting these "high-claims" signals before a policy renewal or a breach occurs, ThreatNG helps organizations secure better coverage rates and avoid the operational failures that drive costly claims.
External Discovery
Claims-based models heavily penalize "unknown unknowns" because unmanaged assets are frequent entry points for ransomware, which drives the largest insurance claims. ThreatNG automates External Discovery to ensure the organization’s "Insurable Surface" matches its actual digital footprint.
Discovering High-Risk Shadow IT: ThreatNG scans the internet to identify assets like "Applications Identified" and "VPNs Identified" that are not in the central registry. If an insurer scans an organization and discovers a forgotten VPN gateway that the CISO was unaware of, it signals a high claims risk. ThreatNG allows the team to find and decommission these assets first.
Identifying Cloud Negligence: Claims involving data leakage often stem from simple misconfigurations. ThreatNG identifies "Files in Open Cloud Buckets," allowing the organization to close these leaks. This directly mitigates the risk of a "Third-Party Liability" claim regarding data privacy negligence.
External Assessment
ThreatNG’s External Assessment capabilities validate the technical controls that are statistically correlated with lower loss ratios. Insurers use these same data points to price risk; ThreatNG allows the organization to optimize them.
Email Security and BEC Prevention
Business Email Compromise (BEC) is a leading cause of cyber insurance claims. ThreatNG provides detailed evidence that the organization has implemented the controls necessary to prevent domain spoofing.
Assessment Detail: The platform assesses the organization's email authentication posture by validating "Email Security: SPF" (Sender Policy Framework) and "Email Security: DMARC" (Domain-based Message Authentication, Reporting, and Conformance) records.
Claims-Based Example: An organization with a "p=none" (monitor only) DMARC policy is statistically more likely to suffer a spoofing attack than one with "p=reject." ThreatNG identifies whether the domain lacks strict DMARC enforcement. By formalizing this as a strict policy, the organization aligns with the "Low Risk" profile used by underwriters, thereby reducing the probability of a BEC claim.
Ransomware Vector Remediation
Ransomware claims are often driven by specific exposures, most notably open remote access ports.
Assessment Detail: ThreatNG performs a "Default Port Scan" to identify exposed, high-risk services (e.g., RDP port 3389, SMB port 445). It also checks for "Invalid Certificates" and "Subdomains with No Automatic HTTPS Redirect."
Claims-Based Example: Historical claims data show a near-perfect correlation between exposed RDP and ransomware infections. ThreatNG identifies a specific IP address with Port 3389 open. By alerting the security team to close this port, ThreatNG eliminates the single most significant indicator of a future ransomware claim, fundamentally altering the organization's risk profile.
Reporting
ThreatNG translates technical findings into the risk metrics that Boards and Insurers understand.
Security Ratings: ThreatNG aggregates findings into Security Ratings (A-F grades). These ratings often mirror the automated scoring systems used by insurance carriers. A report showing an improvement from a "C" to an "A" provides demonstrative evidence of risk reduction that can be used during insurance renewal negotiations.
Financial Risk Mapping: By identifying "ESG Violations" and "Lawsuits" associated with external assets, ThreatNG provides a broader view of liability beyond technical cyber risk, aiding Directors and Officers (D&O) insurance assessments.
Continuous Monitoring
An insurance policy is valid for a year, but the risk changes daily. ThreatNG’s Continuous Monitoring ensures that the organization does not drift into a "High Claims" profile mid-policy.
Drift Detection: If a network engineer accidentally opens a firewall port for testing and forgets to close it, the organization’s claims risk spikes immediately. ThreatNG detects this "Drift" instantly. This allows the organization to maintain the "Standard of Care" promised in the insurance application, thereby preventing potential coverage disputes if a breach occurs during that window of exposure.
Investigation Modules
ThreatNG’s Investigation Modules allow teams to hunt for the precursors to a claim—the "smoke" before the fire.
Domain Intelligence
Investigation Detail: This module analyzes "Domain Name Permutations - Taken" and checks for "Domain Name Permutations - Taken with Mail Record."
Claims Context: Pre-emptive detection of typo-squatted domains allows the organization to block them before they are used in a wire fraud attack. Preventing a single $500,000 wire fraud transfer (a common crime policy claim) yields a massive return on investment and keeps the claims history clean.
Subdomain Intelligence
Investigation Detail: This module identifies specific software versions and "Subdomains Using Deprecated Headers" on the perimeter.
Claims Context: Claims data often show that attackers exploit old, unpatched vulnerabilities (such as Log4j or legacy VPN flaws). ThreatNG identifies these specific "End-of-Life" assets. By removing them, the organization removes the "low-hanging fruit" that attackers—and claims adjusters—look for.
Intelligence Repositories
ThreatNG connects internal findings with external threat reality to prioritize the risks that are actually causing losses in the wild.
Dark Web & Ransomware Correlation: ThreatNG checks if exposed assets are associated with "Dark Web Mentions" or "Ransomware Events." If an organization has an open port that a ransomware group active in its sector is targeting, the "Claims-Based Risk" is critical. ThreatNG highlights this connection, driving immediate action.
Complementary Solutions
ThreatNG acts as the "Technical Evidence Provider" in the risk management ecosystem, feeding verified data into decision-making platforms.
Cyber Insurance Underwriting Platforms
ThreatNG provides the "Outside-In" scan data that powers the underwriting process.
Cooperation: The underwriting platform calculates the premium based on risk. ThreatNG provides the raw data inputs (e.g., "Port 3389 is Closed," "DMARC is Valid," "No Dark Web Credential Leaks"). This cooperation ensures that the premium is priced accurately based on the organization's actual security posture rather than generic industry averages.
Governance, Risk, and Compliance (GRC) Platforms
ThreatNG automates risk likelihood quantification.
Cooperation: The GRC platform manages the "Risk Register." ThreatNG continuously validates the "Likelihood" and "Vulnerability" fields. If ThreatNG detects "Code Secrets Found" in a public repository, it pushes this high-severity finding to the GRC platform, automatically increasing the calculated "Residual Risk" score for that asset owner.
Security Information and Event Management (SIEM)
ThreatNG provides the external context for internal alert correlation.
Cooperation: ThreatNG alerts the SIEM to "Subdomain Takeover" risks or "Default Port Scan" exposures. The SIEM correlates these external flags with internal traffic logs. If traffic is detected moving toward a high-risk asset identified by ThreatNG, the SIEM can trigger an automated response to block the connection, thereby preventing the "Claim Event" (the breach) from executing.
Frequently Asked Questions
How does ThreatNG lower cyber insurance premiums? ThreatNG identifies the specific red flags (such as open RDP ports and a lack of MFA/DMARC) that lead insurers to increase premiums. By finding and fixing these issues before the quote is generated, organizations present a lower-risk profile to the underwriter.
Can ThreatNG help if a claim is denied? Yes. If an insurer attempts to deny a claim by arguing the organization was negligent (e.g., "You didn't patch"), ThreatNG's historical reports provide time-stamped evidence of the organization's continuous monitoring and discovery processes, proving that "Due Care" was exercised.
Does ThreatNG detect ransomware precursors? Yes. By identifying open remote access ports, exposed VPNs, and leaked credentials on the dark web, ThreatNG pinpoints the exact entry vectors used by ransomware gangs, enabling organizations to close the door before an attack occurs.
