Security Rating Ambiguity
Security Rating Ambiguity in the context of cybersecurity refers to the problem in which different security assessment methodologies, tools, vendors, or internal teams assign varying, and often conflicting, risk scores or severity ratings to the same vulnerability, asset, or overall organizational security posture. This inconsistency makes it extremely difficult for security professionals and business leaders to prioritize risks and allocate resources effectively.
Causes of Security Rating Ambiguity
Security Rating Ambiguity arises from fundamental differences in how security posture is measured and interpreted:
1. Inconsistent Scoring Methodologies
The most common cause is reliance on and misapplication of standardized scoring systems rather than on proprietary or contextual models.
CVSS Version Differences: The Common Vulnerability Scoring System (CVSS) has evolved through multiple versions (v2, v3, v4). A vulnerability might receive a High score under CVSS v3. Still, the same flaw might be rated Critical under CVSS v4 due to changes in base metrics, such as attack complexity or required privileges.
Proprietary Vendor Scales: Different security rating service (SRS) vendors use proprietary algorithms that factor in different data sets (e.g., historical breach data, industry peers, financial filings) to generate a score (e.g., an A-F letter grade or a numerical score out of 900). A customer could receive a B from one vendor and a C+ from another for the same set of publicly observed findings.
2. Disparate Contextual Factors
Ambiguity increases when the scoring mechanism does not use or prioritize the same contextual factors.
Missing Asset Criticality: An internal scanner might assign a High severity rating to a vulnerability based solely on its technical impact. However, if that vulnerability is on a non-critical development server, the internal risk team might rate it as Low priority for the business. The conflict is between technical severity and business impact.
Varying Threat Intelligence Integration: One tool might integrate timely threat intelligence (e.g., evidence of active exploitation) and immediately elevate a CVSS Medium score to an Urgent internal priority. Another tool lacking this external context would retain the original Medium score, creating ambiguity about the actual required remediation speed.
3. Subjective Interpretation and Tuning
Even when using the same tool, human judgment and tuning can introduce ambiguity.
Policy Differences: Two security teams in the same organization might set their vulnerability-scanning policies differently—one excludes findings for older operating systems, the other includes them. This results in two vastly different reports and two different security ratings for the same overall environment.
Impact on Cyber Risk Management
The consequence of Security Rating Ambiguity is the inability to establish a single, trusted source of truth regarding risk. This leads to:
Misdirected Resources: Teams waste time patching technically severe flaws that pose low business risk, while neglecting contextually severe flaws that are actively being exploited.
Erosion of Trust: Business executives lose confidence in the security program when they receive conflicting risk reports from different sources (internal reports versus third-party vendor reports).
Ineffective Prioritization: Security teams cannot effectively use vulnerability management systems because the ambiguity makes it impossible to build a clear, defendable work queue.
ThreatNG is specifically designed to resolve Security Rating Ambiguity by enforcing a standardized, contextualized, and continuously updated risk score that integrates technical severity, business criticality, and real-world threat intelligence into a single, unambiguous metric. It moves away from relying solely on static CVSS scores or subjective human judgment.
ThreatNG's Solution to Ambiguity
1. External Discovery and Continuous Monitoring
These modules provide a comprehensive, up-to-date data foundation to ensure consistent ratings across all assets. Ambiguity often starts when different tools have different views of the asset inventory.
External Discovery: By continuously and autonomously discovering all internet-facing assets—including cloud instances, forgotten subdomains, and third-party vendor exposures—ThreatNG ensures that every asset is included in the risk calculation. This eliminates ambiguity that arises from incomplete asset inventories across different assessment tools.
Continuous Monitoring: ThreatNG continuously monitors asset changes (e.g., a port opening or a new certificate deployment). This eliminates the ambiguity caused by stale data in periodic reports, ensuring the risk score reflects the environment's current state at all times.
2. External Assessment and Intelligence Repositories (The Contextual Engine)
These modules are the core mechanism ThreatNG uses to harmonize conflicting scores by providing non-negotiable, prioritized context.
External Assessment
This module provides the necessary technical and exposure context to move beyond a generic CVSS score and determine the true exploitability, which removes the ambiguity of "Is this finding real or just theoretical?"
Detailed Examples of External Assessment:
Exploitability Validation: A vulnerability (e.g., Log4Shell) is rated Critical (CVSS 10.0). However, ThreatNG's assessment scans the specific asset and determines that the vulnerable function is not exposed to the internet. The ThreatNG score is then adjusted to reflect Low exposure risk, resolving the ambiguity between the theoretical high technical score and the actual low external risk.
Configuration Integrity Check: An asset is found to have weak SSL/TLS cipher suites. ThreatNG’s assessment provides the mitigation context that the server configuration is outdated. This finding is weighted differently than if the cipher suite were weak and the certificate were expired, providing a more granular and less ambiguous risk rating for the hardening effort required.
Service Context Mapping: ThreatNG identifies an open database port. The assessment further determines that this database is running an outdated version of SQL. This combined service and version context means the resulting risk score is uniquely specific to the exploitability of that particular database version, eliminating the ambiguity of treating it as if it were a newer, patched version.
Intelligence Repositories
These repositories integrate active threat data to resolve the ambiguity between theoretical risk and imminent danger.
ThreatNG automatically integrates findings with a constantly updated repository of global threat intelligence. Suppose the platform detects a vulnerability that is also cataloged as actively weaponized or part of a current attack campaign. In that case, the system automatically applies a significant multiplier to the risk score. This resolves the ambiguity for security teams by providing a single, clear signal on what must be prioritized immediately.
3. Investigation Modules and Reporting
These components translate the contextualized risk score into clear, actionable mandates, eliminating ambiguity in workflow and communication.
Investigation Modules: When a high score is assigned, the module allows analysts to drill down to view all contributing factors simultaneously (CVSS, business-criticality tag, active threat indicator, and exposure level). This comprehensive view eliminates the need to manually correlate information across four tools, providing a single source of truth for the final risk rating. Detailed Examples of Investigation Modules in Use:
Risk Justification: An analyst is questioned about a High severity score assigned to a new cloud resource. The Investigation Module displays the justification: "Scored High because: A) Exposed API key (Assessment finding), B) Asset tagged Critical (Business Context), and C) API key is linked to a known threat group's TTPs (Intelligence Context)." This clear, multi-faceted justification resolves ambiguity and speeds up consensus for remediation.
Policy Review: The module can show why a specific risk was downgraded. For instance, a firewall rule vulnerability might be rated Low because the Investigation Module confirms that the asset is tagged as "Isolated Test Network" (Contextual Override Policy), which justifies the deprioritization.
Reporting: ThreatNG's reports present the final, contextualized risk score rather than a raw list of technical findings. This is crucial for executive buy-in, as it provides a clear, unambiguous picture of the organization's most significant threats, enabling business decisions to be made with a single, reliable metric.
4. Working with Complementary Solutions
ThreatNG collaborates with other security and business solutions to ensure the risk score is consistently and actionably aligned across the enterprise.
Cooperation with Governance, Risk, and Compliance (GRC) Tools: ThreatNG shares its contextualized risk scores and detailed remediation proofs with GRC platforms. This cooperation eliminates ambiguity in compliance reporting, as the GRC tool doesn't have to guess or average multiple conflicting data points; it receives a single, validated risk metric from ThreatNG for regulatory reporting and audit trails.
Example: ThreatNG provides a consolidated, unambiguous risk score of 8.5/10 for "External Customer Data Exposure." The GRC tool automatically uses this score as evidence of the current risk level associated with PCI compliance, avoiding the internal debate over which vulnerability scanner's report to trust.
Cooperation with Endpoint Detection and Response (EDR) Systems: ThreatNG feeds its findings on exposed assets and known exploit chains to EDR systems. The EDR can use this external context to refine its internal monitoring and alerting.
Example: ThreatNG discovers an exposed remote desktop service with a medium-rated vulnerability. It sends this asset and exposure context to the EDR. Suppose the EDR detects an unusual login attempt on that specific, contextually prioritized asset. In that case, it can automatically escalate the incident response, eliminating the ambiguity of whether the alert is a routine event or a targeted attack.
