Multi-Source Data Fusion
Multi-Source Data Fusion in the context of cybersecurity is the process of collecting, normalizing, and correlating disparate streams of security, threat, and business data from various independent sources to generate a single, cohesive, and significantly more informative view of an event or risk. The goal is to create a context that is greater than the sum of its parts, enabling rapid, precise decision-making.
The Fusion Process
This process is critical to overcoming the challenges posed by data silos and the Crisis of Context. It typically involves three phases:
1. Data Collection and Normalization
This initial phase involves gathering data from all available sources, which can include both internal and external telemetry:
Internal Sources: Logs from firewalls, endpoint detection and response (EDR) agents, identity and access management (IAM) systems, and network traffic analyzers.
External Sources: Threat intelligence feeds, dark web forums, public vulnerability databases (like CVEs), domain registration records (WHOIS), and financial or legal filings.
Once collected, the data must be normalized—transformed into a standard format and schema—so that fields such as "user name," "asset IP," and "severity" are consistently understood across all platforms, regardless of the source.
2. Correlation and Association
This is the core fusion phase, where algorithms and logic are used to find relationships between the normalized data points:
Temporal Correlation: Linking events that occur within a close timeframe.
Identity Correlation: Linking an observed behavior (e.g., a suspicious network connection) to a specific user, asset, or application ID across different systems.
Contextual Correlation: Associating a technical finding (like a vulnerable software version) with external context (like a known exploit or a published threat actor tactic).
3. Synthesis and Attribution
The final phase produces the actionable intelligence. By correlating various sources, the system can synthesize a high-certainty verdict, achieving Irrefutable Attribution.
Example: A log from an EDR tool flags a connection to a suspicious IP (Source 1). Data fusion correlates this with:
A threat intelligence feed confirms the IP is a known command-and-control server (Source 2).
A dark web source reported that the associated user's credentials were recently compromised (Source 3).
An internal asset inventory showing the affected system is a critical financial server (Source 4).
The fusion of these four data points transforms an ambiguous "suspicious connection" alert into a high-priority "Confirmed Breach Attempt on a Critical Asset using Stolen Credentials," providing the context needed for immediate, justified containment.
ThreatNG is purpose-built to use Multi-Source Data Fusion through its patent-backed Context Engine™ to resolve the Crisis of Context and the Attribution Chasm. This fusion process iteratively correlates external technical security findings with decisive legal, financial, and operational context. By blending data from numerous internal modules and external intelligence repositories, ThreatNG achieves Legal-Grade Attribution—the absolute certainty required to justify security investments and accelerate remediation.
ThreatNG’s Use of Multi-Source Data Fusion
1. External Discovery
The fusion process begins with external unauthenticated discovery, which generates the raw data from various vectors of the target's attack surface.
Fusion Data Source 1: Technology Stack: ThreatNG performs exhaustive, unauthenticated discovery of nearly 4,000 technologies.
Fusion Data Source 2: Domain and Subdomains: It finds all associated subdomains and performs Domain Name Permutations to find typosquatting domains.
Example of Help (Initial Fusion): ThreatNG discovers a subdomain running a specific version of a WordPress Content Management System (Technology Stack). This technical finding (Source 1) is immediately fused with the subdomain's IP Intelligence (Source 2) and Certificate Intelligence (Source 3) to establish the geographical location and issuance status, forming the foundational context for risk assessment.
2. Intelligence Repositories (DarCache)
The Intelligence Repositories (DarCache) serve as the foundation for the fusion process, providing external, global, and highly specialized context.
Fusion Data Sources: These repositories include the Dark Web, Compromised Credentials (DarCache Rupture), Ransomware Groups and Activities, Vulnerabilities (NVD, EPSS, KEV, Verified PoC Exploits), and SEC Form 8-Ks.
Example of Help (Vulnerability Fusion): A technical finding (e.g., a software version on a subdomain) is cross-referenced with four intelligence sources for a complete risk context:
NVD (DarCache NVD): Provides the technical details and CVSS severity score.
KEV (DarCache KEV): Confirms if it is actively being exploited in the wild.
EPSS (DarCache EPSS): Estimates the probabilistic likelihood of future exploitation.
DarCache eXploit: Provides a link to a Verified Proof-of-Concept Exploit. This fusion transforms a simple technical alert into a conclusive risk priority, achieving Irrefutable Attribution.
3. External Assessment and Security Ratings
The platform's security ratings are the result of Multi-Source Data Fusion, which correlates technical data with business risk metrics.
Detailed Examples of External Assessment Fusion:
Supply Chain & Third Party Exposure: This rating fuses five primary external sources:
Cloud Exposure: Externally identified cloud environments.
Domain Name Record Analysis: Enumeration of vendors within domain records.
SaaS Identification: Identification of cloud and SaaS vendors.
Subdomains: Identification of "other" cloud vendors.
Technology Stack: The total number of technologies discovered.
Fusion Result: By combining the technical findings of exposed cloud assets and the technology count with the identities of the vendors involved, the platform provides a complete, contextual rating of supply chain risk.
Brand Damage Susceptibility: This rating fuses technical domain risks with external sentiment and legal context:
Domain Name Permutations: Available and taken permutations.
ESG Violations: Publicly disclosed offenses.
Lawsuits: Publicly disclosed lawsuits.
Negative News: External reports.
Fusion Result: Finding a domain permutation using "offensive language" keywords (technical finding) and correlating it with a recent, publicly disclosed Lawsuit (legal context) provides Irrefutable Attribution that the organization faces an immediate, credible brand reputation risk.
4. Investigation Modules
The modules enable analysts to use the fused data for targeted research and validation actively.
Detailed Examples of Investigation Module Fusion:
Subdomain Intelligence: This module fuses various technical assessments for a single subdomain:
Header Analysis: Checks for missing security headers (e.g., Content-Security-Policy).
Subdomain Cloud Hosting: Identifies the specific cloud provider (e.g., AWS, Azure).
Known Vulnerabilities: Cross-references exposed technologies with vulnerability intelligence.
Fusion Result: An analyst investigates a subdomain and finds it has missing security headers (Source 1), is hosted on a specific AWS service (Source 2), and runs a version of a software with a confirmed KEV vulnerability (Source 3). This fusion gives the decisive context that the vulnerability is exploitable and resides on a critical cloud asset.
Archived Web Pages: This module groups all archived files (HTML, JSON, Emails, Login Pages, etc.).
Fusion Result: Discovering an archived login page and a related username (Source 1) can be fused with a match in the Compromised Credentials DarCache (Source 2) and an NHI Email Exposure finding (Source 3). This synthesis provides Irrefutable Attribution that a specific set of valid credentials for an exposed login page exists and is compromised.
5. Continuous Monitoring and Reporting
The reporting capabilities are designed to communicate the product of the data fusion in an actionable format.
Example of Help: All raw findings are automatically translated and correlated with specific MITRE ATT&CK techniques (strategic context). The system fuses the technical conclusions (e.g., a leaked credential and an open port) with this strategic context to generate a Prioritized Report that allows security leaders to justify security investments to the boardroom with business context.
ThreatNG and Complementary Solutions
ThreatNG provides a layer of certainty intelligence that significantly enhances other security investments.
Security Orchestration, Automation, and Response (SOAR) Solutions:
Cooperation: ThreatNG provides high-certainty, Legal-Grade Attribution that allows SOAR playbooks to execute complex, high-impact actions automatically, without waiting for human confirmation.
Example: An ambiguous alert about a "new external network connection" would typically cause automation to stall. However, suppose ThreatNG fuses that connection with a confirmed Compromised Credential from DarCache Rupture, an exposed Private IP, and a public SEC 8-K filing describing a similar prior breach. In that case, the SOAR solution receives the Irrefutable Attribution needed to confidently trigger an automated, high-severity action, such as blocking the entire ASN or forcing a global password reset.
Security Monitoring (SIEM) Solutions:
Cooperation: ThreatNG provides external threat intelligence and financial/legal context to enrich raw log data collected by SIEMs.
Example: A SIEM registers an attempted access to a sensitive application. ThreatNG’s Context Engine™ instantly fuses the technical logs with external context: the user's IP is associated with a known threat actor group tracked in DarCache Ransomware (external threat context), and the targeted application belongs to a third-party vendor with a failing Supply Chain & Third Party Exposure rating (external risk context). This fusion provides the SIEM with the critical, decisive information needed to elevate the internal log from a low-priority notification to an immediate, confirmed threat incident.
