Autonomous Risk Correlation
Autonomous Risk Correlation in cybersecurity refers to the automated, continuous, and intelligent process of identifying, linking, and analyzing relationships between various security events, vulnerabilities, asset attributes, and threat intelligence indicators to uncover hidden patterns, systemic risks, and potential attack pathways. It goes beyond simple data aggregation or rule-based alerting using advanced analytical techniques to infer connections and derive deeper insights without constant manual intervention.
The Problem it Solves:
In today's complex threat landscape, organizations face an overwhelming volume of security data from disparate sources:
Alert Fatigue: Security Operations Centers (SOCs) are inundated with millions of alerts daily, many of which are false positives or low-priority individual events.
Siloed Information: Data from different security tools (e.g., vulnerability scanners, endpoint detection and response, identity management, threat intelligence feeds, firewalls) often remains isolated, preventing a holistic view of risk.
Hidden Connections: Attackers often chain seemingly minor vulnerabilities or events to achieve a significant compromise. Manually identifying these subtle correlations across vast datasets is nearly impossible for human analysts.
Dynamic Environments: Attack surfaces and threats constantly change, requiring continuous re-evaluation of risk relationships.
Autonomous Risk Correlation addresses these challenges by acting as an intelligent "connector" that can automatically make sense of this chaos.
How it Works:
Autonomous Risk Correlation typically relies on several foundational elements and processes:
Comprehensive Data Ingestion:
Collecting data from various sources: security logs, asset inventories, vulnerability scan results, network telemetry, endpoint data, identity information, cloud configurations, and external threat intelligence feeds (e.g., IOCs, CVEs, threat actor TTPs).
This data is often normalized and enriched to ensure consistency and usability.
Entity Resolution and Graph Construction:
A critical prerequisite is the ability to unify disparate data points into coherent "entities" (e.g., a specific user, a unique device, a single application instance) regardless of how many different IDs or names they might have across various systems.
These entities and their attributes (e.g., operating system, vulnerabilities, software versions) are then modeled as a graph, where entities are nodes and their relationships are edges.
Automated Relationship Discovery and Inference:
This is the core of "autonomous" correlation. Instead of predefined, static rules, the system intelligently identifies connections. This can involve:
Direct Relationships: A vulnerability exists on a server; a user is logged into a device.
Indirect Relationships: A malicious IP address communicated with a server, and that server hosts a critical application, which indirectly risks the application.
Temporal Correlation: Events occurring in a specific sequence or time window suggest a coordinated attack.
Behavioral Analysis: Identifying deviations from standard activity patterns for a given entity.
Threat Intelligence Overlays: Automatically mapping known malicious indicators (IPs, domains, hashes) or TTPs (e.g., a specific ransomware gang's methods) to an organization's particular assets and vulnerabilities.
Risk Aggregation and Prioritization:
Once relationships are mapped, the system aggregates risk. It can calculate an entity's combined risk score based on all its associated vulnerabilities, exposures, and threat intelligence context.
It identifies high-priority risks by recognizing "kill chains" or attack pathways – sequences of connected vulnerabilities and exposures that an attacker could exploit to reach a critical asset.
This prioritization helps security teams focus on the issues that pose the most significant and immediate threat.
Continuous Monitoring and Adaptive Learning:
The system operates continuously, constantly ingesting new data and re-evaluating relationships as the environment or threat landscape changes.
Some advanced systems may incorporate machine learning to adapt and improve their correlation logic over time, recognizing new patterns or evolving threats.
Benefits in Cybersecurity:
Enhanced Threat Detection: Uncovers complex, multi-stage attacks that would be missed by individual alerts, such as an attacker chaining a publicly exposed vulnerability with stolen credentials.
Reduced Alert Fatigue: Filters out noise by identifying and elevating truly critical, interconnected risks, allowing security analysts to focus their efforts.
Proactive Risk Management: Identifies potential attack pathways before they are exploited, enabling proactive mitigation and hardening of defenses.
Faster Incident Response: Provides immediate, comprehensive context around a security incident, detailing all affected assets, users, and related vulnerabilities, accelerating containment and remediation.
Optimized Security Posture: Helps security teams understand systemic weaknesses and dependencies, informing strategic security investments and resource allocation.
Improved Visibility: Creates a holistic, unified view of the security landscape, breaking down data silos between different security tools.
Autonomous Risk Correlation is a critical capability for modern cybersecurity. It enables organizations to move from a reactive, alert-driven defense to a more proactive, intelligent, and context-aware security posture.
ThreatNG provides Autonomous Risk Correlation by continuously discovering, assessing, and analyzing external digital assets and risks, automatically identifying and linking vulnerabilities, exposures, and threat intelligence without manual intervention. This is achieved by building an interconnected view of an organization's external attack surface, where different elements are automatically linked to reveal compound risks and potential attack paths.
ThreatNG’s External Discovery: The Foundation of Correlation
ThreatNG's external, unauthenticated discovery is the initial data ingestion phase for autonomous risk correlation. It systematically uncovers numerous external assets, each becoming a data point that can be correlated with others.
Diverse Asset Discovery: ThreatNG identifies many external assets, including domains, subdomains, public IP addresses, cloud services (AWS, Azure, GCP), SaaS solutions, code repositories, and mobile applications. This broad collection ensures that all potential pieces of a risk correlation puzzle are present.
Example: ThreatNG discovers dev.example.com (a subdomain), an associated IP address, and an exposed SSH port on that IP. Individually, these are findings. Autonomous correlation begins by linking these elements.
External Assessment: Automatically Linking Risks to Context
ThreatNG's external assessment capabilities are where the autonomous correlation truly manifests. They automatically link discovered assets to various risk types and provide context for their severity.
Web Application Hijack Susceptibility & Subdomain Takeover Susceptibility: ThreatNG analyzes external attack surface and digital risk intelligence, including DNS records and SSL certificate statuses. It automatically correlates misconfigurations or outdated records with the potential for compromise.
Example: ThreatNG identifies a dangling DNS record for legacy.example.com. It automatically correlates this specific record with the subdomain takeover susceptibility, linking the dormant DNS record to a high-risk exposure for the example.com domain.
Code Secret Exposure: ThreatNG discovers code repositories and investigates their contents for sensitive data. It autonomously correlates the presence of a repository with the exposure level of sensitive data within it.
Example: ThreatNG discovers a public GitHub repository linked to "Example Company". It then autonomously scans the contents and finds an "AWS Access Key ID". This immediately correlates the public code repository (the asset) with the critical exposure of a credential (the risk), creating a clear, direct risk relationship for "Example Company."
BEC & Phishing Susceptibility: This score is derived from correlating Domain Intelligence, Email Intelligence (email security presence and format prediction), and Dark Web Presence (Compromised Credentials).
Example: ThreatNG identifies that example.com has poor SPF records (a configuration weakness) and finds multiple compromised credentials for john.doe@example.com (an employee identity) in its DarCache Rupture repository. ThreatNG autonomously correlates these findings: the domain's email configuration weakness is linked to the exposed employee credentials, leading to a higher BEC & Phishing Susceptibility score. This shows how an infrastructure misconfiguration correlates with identity risk.
Data Leak Susceptibility: This correlates Cloud and SaaS Exposure, Dark Web Presence (Compromised Credentials), and Sentiment and Financials (Lawsuits and SEC Form 8-Ks).
Example: ThreatNG identifies an open AWS S3 bucket belonging to "Example Cloud Services". It automatically correlates this with any mentions of "Example Cloud Services" in SEC Form 8-Ks related to past data leaks or lawsuits (from Sentiment and Financials findings). This autonomous correlation highlights that the open bucket is not an isolated finding but part of a historical pattern of data exposure for that entity, elevating its risk.
Reporting and Continuous Monitoring: Actionable Correlation
ThreatNG's reporting and continuous monitoring capabilities ensure that the autonomous risk correlations are presented clearly and updated in real-time, providing actionable intelligence.
Prioritized Reports: ThreatNG's prioritized reports (High, Medium, Low, and Informational) are a direct output of its autonomous correlation engine. Risks are ranked not just by individual severity but by their interconnectedness and potential impact as revealed by correlation.
Example: A report might flag a "High" risk because an exposed RDP port (discovered asset) is found on a server that has a known vulnerability (from DarCache NVD) that is also listed in DarCache KEV (actively exploited) and is a common target for ransomware gangs (from DarCache Ransomware). ThreatNG autonomously correlates these disparate pieces of information (asset, vulnerability, exploit status, threat actor TTP) to present a singular, highly prioritized risk that directly informs remediation efforts.
Continuous Monitoring: ThreatNG continuously monitors all organizations' external attack surface, digital risk, and security ratings. This means that discoveries, asset configuration changes, or intelligence repositories' updates are autonomously correlated with existing data, keeping the risk assessment dynamic and accurate.
Example: If a new vulnerability (from DarCache NVD) is published that affects a specific version of Apache, and ThreatNG has already discovered a public-facing web server running that Apache version, it autonomously correlates this new threat intelligence with the existing asset, immediately updating the server's risk posture and potentially triggering an alert.
Investigation Modules: Exploring Correlated Risks
ThreatNG's investigation modules enable security professionals to explore the results of autonomous risk correlation and understand the underlying connections and data points.
Domain Intelligence: This module allows users to delve into a domain's profile, including DNS records, associated vendors, and exposed ports. It helps to correlate these elements to reveal attack paths autonomously.
Example: An analyst investigates marketing.example.com. Domain Intelligence shows it's hosted on a specific IP with an exposed database port (e.g., PostgreSQL). ThreatNG autonomously correlates this specific exposed database with its known vulnerabilities (from DarCache NVD) and potentially with any compromised credentials found in that particular database type (from DarCache Rupture), creating a correlated view of the database's risk posture.
Sensitive Code Exposure: This module discovers exposed code repositories and sensitive data.
Example: ThreatNG identifies a public repository containing an "API Key." It autonomously correlates this exposed key with any known services that use it (e.g., Stripe, Google Cloud Platform), identifying the potential blast radius of this single exposure.
Intelligence Repositories (DarCache): The Engine for Autonomous Correlation
ThreatNG's DarCache intelligence repositories are fundamental to its autonomous risk correlation. They provide the vast datasets against which discovered assets and vulnerabilities are automatically mapped and enriched.
DarCache Vulnerability (NVD, EPSS, KEV, eXploit): This repository allows ThreatNG to correlate discovered assets with known vulnerabilities and their exploitability autonomously.
Example: ThreatNG discovers a server running a specific software version. It autonomously correlates this software version with CVEs in DarCache NVD. If a matched CVE is also in DarCache KEV (actively exploited) and has an associated Verified Proof-of-Concept (PoC) exploit in DarCache eXploit, ThreatNG autonomously correlates these facts, elevating the risk significantly and providing direct pathways for potential exploitation.
DarCache Rupture (Compromised Credentials): This repository enables autonomous correlation of external digital identities with known breaches.
Example: ThreatNG's discovery identifies employee@example.com. It autonomously checks DarCache Rupture and finds a match. This automatically correlates the employee's identity with a known compromise, impacting the organization's BEC & Phishing Susceptibility score.
DarCache Ransomware: This repository tracks ransomware gangs and their activities.
Example: ThreatNG identifies an exposed sensitive port on an organization's network. It autonomously correlates this exposure with known TTPs and targets of ransomware gangs listed in DarCache Ransomware, automatically assessing the "Breach & Ransomware Susceptibility".
Synergies with Complementary Solutions
ThreatNG's autonomous risk correlation capabilities enhance a broader security ecosystem by providing context-rich, pre-correlated intelligence.
Security Information and Event Management (SIEM) / Security Orchestration, Automation, and Response (SOAR) Platforms: ThreatNG can feed its autonomously correlated risk findings into a SIEM. For instance, if ThreatNG autonomously correlates a public-facing web server (an asset) with an actively exploited vulnerability (from DarCache KEV) and a specific threat actor known to target that vulnerability (from DarCache Ransomware), this enriched, correlated alert can be sent to a SIEM. A SOAR playbook can then automatically initiate immediate actions like blocking the source IP, isolating the server, or triggering a high-priority ticket for patch management, all based on ThreatNG's intelligent correlation.
Vulnerability Management Platforms: ThreatNG's autonomously correlated vulnerability data, especially regarding exploitability (EPSS, KEV, PoC exploits), greatly refines vulnerability prioritization. If ThreatNG autonomously correlates a specific CVE on a public-facing system with an active exploit and a high likelihood of weaponization, this intelligence can be imported. A vulnerability management platform can then use this context to automatically prioritize that specific vulnerability on that specific asset over others, ensuring remediation efforts are focused on the most critical and currently exploitable risks.
Threat Intelligence Platforms (TIPs): ThreatNG's external discovery and assessment enrich existing TIPs by providing specific context for applying generic intelligence. ThreatNG can feed back autonomously correlated findings (e.g., specific assets linked to specific IOCs from DarCache Ransomware) to a TIP, allowing the TIP to further enhance its own intelligence feeds and make them more relevant to the organization's unique external footprint.
Attack Surface Management (ASM) Solutions: While ThreatNG is an ASM, its autonomous correlation capabilities elevate it beyond simple asset inventory. Complementary ASM solutions focused on internal or cloud-native aspects could benefit from ThreatNG's external correlations, providing a more complete picture of risk by linking external exposures to internal dependencies. For example, if ThreatNG autonomously correlates an exposed external API gateway (asset) with a severe vulnerability (from DarCache Vulnerability), an internal ASM solution could then prioritize scanning and assessing the internal services accessible via that gateway.
By autonomously correlating vast amounts of external data, ThreatNG transforms raw findings into actionable, prioritized risks, significantly improving an organization's ability to detect, understand, and mitigate external threats.
