Predictive Risk Prioritization

Predictive Risk Prioritization in cybersecurity is a proactive strategy that focuses on identifying, assessing, and ranking security risks based on their anticipated likelihood and potential impact, before they are actively exploited or cause harm. Rather than simply reacting to discovered vulnerabilities or incidents, this approach uses data-driven insights and advanced analytics to forecast which risks pose the greatest future threat to an organization.

Here's a detailed breakdown:

Core Concepts

1. Shift from Reactive to Proactive: Traditional risk management typically involves addressing risks as they are discovered or after an incident has occurred. Predictive risk prioritization moves beyond this by anticipating future risks.

2. Data-Driven Decisions: It relies heavily on analyzing vast amounts of historical and current data to identify patterns and trends. This data can come from various sources, including:

   • Vulnerability databases (e.g., CVE, NVD).

   • Threat intelligence feeds (information on threat actor TTPs, campaigns, and indicators of compromise).

   • Internal security logs (network traffic, endpoint activity, authentication logs).

   • Incident response data (post-mortem analyses of past breaches).

   • Industry-specific threat landscape reports.

   • Publicly available information (OSINT) on adversary activity.

3. Advanced Analytics and Machine Learning: The "predictive" element largely stems from the use of sophisticated analytical techniques, including machine learning algorithms. These algorithms can:

   • Identify Leading Indicators: Pinpoint subtle changes or precursors that suggest a higher likelihood of a future attack.

   • Forecast Likelihood: Estimate the probability that a specific vulnerability will be exploited, or that a particular attack vector will be used, within a defined timeframe. Factors considered include active exploitation in the wild, the availability of proof-of-concept code, ease of exploitation, and threat actor focus.

   • Assess Potential Impact: Quantify the potential damage an exploited risk could cause, considering factors like data sensitivity, system criticality, business disruption, and regulatory implications.

   • Correlate Disparate Data: Find hidden relationships between seemingly unrelated security events or data points to reveal emerging risks.

How it Works

The process of predictive risk prioritization typically involves several steps:

1. Asset Inventory and Contextualization: Identify all critical assets (systems, data, applications, identities) and understand their business value, criticality, and interdependencies. This context is essential for assessing potential impact.

2. Continuous Data Collection: Gather security data from both internal and external sources on an ongoing basis.

3. Threat Landscape Monitoring: Continuously monitor the evolving threat landscape, including new vulnerabilities, emerging attack techniques, and the activities of specific threat actors.

4. Risk Modeling and Scoring: Use algorithms to process the collected data and generate risk scores for each identified vulnerability or potential threat. This score often combines the predicted likelihood of exploitation with the possible impact on the organization's specific assets.

5. Prioritization and Remediation Planning: Rank risks based on their predictive scores. This allows security teams to focus their limited resources on the threats that are most likely to materialize and cause the most significant damage. Remediation plans are then developed and executed for these high-priority risks.

6. Continuous Feedback and Refinement: The models are constantly updated and refined with new data, including the outcomes of past predictions (e.g., if a predicted high-risk vulnerability was indeed exploited or successfully mitigated). This iterative process improves the accuracy of future predictions.

Key Benefits

• Proactive Security Posture: Shifts organizations from a reactive "whack-a-mole" approach to a forward-looking, preventative one.

• Optimized Resource Allocation: Ensures that security teams and resources are focused on the most critical and probable threats, maximizing efficiency and effectiveness.

• Reduced Attack Surface: By anticipating and addressing vulnerabilities before they are exploited, organizations can significantly shrink their attack surface.

• Faster Remediation: Knowing which risks are most likely to be exploited enables preemptive patching, configuration changes, or defensive adjustments.

• Improved Business Resilience: Minimizes the likelihood and impact of successful cyberattacks, contributing to business continuity.

• Better Decision-Making: Provides executives and security leaders with data-driven insights to make informed strategic decisions about security investments and risk tolerance.

• Detection of Emerging Threats: Can identify novel attack vectors or zero-day threats by recognizing anomalous patterns that deviate from established baselines.

Predictive risk prioritization transforms the vast amount of security data into actionable intelligence, allowing organizations to intelligently "place their bets" on where to invest their defensive efforts to achieve the most significant protective outcome.

ThreatNG, an all-in-one external attack surface management, digital risk protection, and security ratings solution, offers comprehensive capabilities that directly contribute to effective Predictive Risk Prioritization. It helps organizations anticipate and mitigate risks by focusing on external, unauthenticated perspectives, much like an attacker would.

ThreatNG's Role in Predictive Risk Prioritization

ThreatNG’s approach to external discovery and assessment provides the foundational data and insights necessary for anticipating future risks. Continuous monitoring of an organization's external footprint and analysis of potential vulnerabilities enables a proactive security posture.

External Discovery

ThreatNG performs purely external, unauthenticated discovery without requiring connectors. This is crucial for Predictive Risk Prioritization as it mirrors an attacker's initial reconnaissance efforts. By identifying an organization's internet-facing assets from an outside-in perspective, ThreatNG helps identify potential entry points that adversaries might target. For example, it can discover forgotten or shadow IT assets that an organization may not even be aware of, but which an attacker could find and exploit. This discovery process provides the initial dataset for predicting where vulnerabilities and risks are likely to emerge.

External Assessment

ThreatNG conducts a wide range of external assessments that are vital for predictive analysis:

• Web Application Hijack Susceptibility: ThreatNG analyzes parts of a web application accessible from the outside world to identify potential entry points for attackers. For Predictive Risk Prioritization, this means foreseeing which web applications are most likely to be targeted for hijacking attempts, allowing an organization to strengthen those defenses and prioritize web application security efforts proactively.

• Subdomain Takeover Susceptibility: This evaluation assesses a website's susceptibility to subdomain takeover by analyzing its subdomains, DNS records, and SSL certificate statuses. This helps predict which subdomains are at risk of being hijacked and used for malicious purposes, such as phishing campaigns, allowing the organization to prioritize remediation before an attack occurs.

• BEC & Phishing Susceptibility: Derived from sentiment and financial findings, Domain Intelligence, and Dark Web Presence. ThreatNG can predict an organization's vulnerability to Business Email Compromise (BEC) and phishing attacks by identifying common spoofing vectors or leaked credentials, enabling the organization to prioritize and implement targeted awareness training or email security enhancements.

• Brand Damage Susceptibility: This assessment is derived from attack surface intelligence, digital risk intelligence, ESG Violations, Sentiment and Financials (Lawsuits, SEC filings, SEC Form 8-Ks, and Negative News), and Domain Intelligence. ThreatNG helps predict potential brand-damaging events by identifying public negative sentiment or legal issues that cybercriminals might use in extortion or reputational attacks, enabling organizations to prioritize public relations and defensive measures.

• Data Leak Susceptibility: This is based on external attack surface and digital risk intelligence, including Cloud and SaaS Exposure, Dark Web Presence (Compromised Credentials), Domain Intelligence, and Sentiment and Financials (Lawsuits and SEC Form 8-Ks). ThreatNG helps predict where data leaks are most likely to occur, whether due to exposed cloud services or compromised credentials on the dark web, allowing for preemptive remediation and prioritization of data protection efforts. For instance, if compromised credentials tied to a specific cloud service are found on the dark web, ThreatNG can predict a higher likelihood of a data breach originating from that service, leading to prioritized access review and credential rotation.

• Cyber Risk Exposure: This considers parameters such as certificates, subdomain headers, vulnerabilities, sensitive ports, code secret exposure, cloud and SaaS exposure, and compromised credentials on the dark web. ThreatNG's ability to identify sensitive ports, vulnerabilities, and exposed code repositories helps predict specific avenues attackers might use for initial access or privilege escalation. For example, if it identifies an outdated certificate on a sensitive port, it predicts a higher risk of an attacker exploiting that weakness, thus prioritizing its remediation.

• ESG Exposure: ThreatNG rates organizations based on discovered environmental, social, and governance (ESG) violations through its external attack surface and digital risk intelligence findings. This can predict the likelihood of activist-driven cyberattacks or reputational damage based on an organization's public ESG footprint, allowing for prioritization of risk mitigation in those areas.

• Supply Chain & Third Party Exposure: Derived from Domain Intelligence (enumeration of vendor technologies), Technology Stack, and Cloud and SaaS Exposure. ThreatNG can predict risks originating from an organization's supply chain by identifying vulnerabilities in third-party technologies or cloud services they utilize, enabling proactive communication with vendors or exploring alternative solutions. For example, if a critical vendor uses an outdated technology stack with known vulnerabilities, ThreatNG can flag this as a potential supply chain attack vector, prioritizing communication with that vendor.

• Breach & Ransomware Susceptibility: This assessment is derived from external attack surface and digital risk intelligence, which includes domain intelligence (exposed sensitive ports, exposed private IPs, and known vulnerabilities), dark web presence (compromised credentials and ransomware events and gang activity), and sentiment and financials (SEC Form 8-Ks). By tracking ransomware gang activity on the dark web and identifying an organization's exposed sensitive assets, ThreatNG can predict the likelihood of a ransomware attack and even identify potential ransomware groups that might target the organization, enabling the prioritization of specific defensive measures and effective ransomware defenses.

• Mobile App Exposure: ThreatNG evaluates how exposed an organization’s mobile apps are through the discovery of them in marketplaces and the content within them, such as access credentials, security credentials, and platform-specific identifiers. This helps predict the risk of mobile app-specific attacks or data leaks due to exposed secrets within the app code. For instance, if an AWS API Key is found within a publicly available mobile app, ThreatNG predicts a high susceptibility to cloud environment compromise, prioritizing a review of the mobile app's security.

• Positive Security Indicators: This feature identifies and highlights an organization's security strengths, such as Web Application Firewalls or multi-factor authentication. It validates these positive measures from the perspective of an external attacker, providing objective evidence of their effectiveness. For Predictive Risk Prioritization, understanding these strengths enables the allocation of resources more effectively, focusing on weaker areas while identifying where defenses are robust and likely to deter certain types of attacks. It provides a more balanced view, allowing predictions to factor in existing mitigations and prioritize risks where positive controls are absent or weak.

Reporting

ThreatNG provides various reports, including Executive, Technical, Prioritized (High, Medium, Low, and Informational), Security Ratings (A through F), Inventory, Ransomware Susceptibility, U.S. SEC Filings, and External GRC Assessment Mappings (PCI DSS and POPIA). These reports are crucial for Predictive Risk Prioritization as they translate complex assessment data into actionable insights for different stakeholders. For example, the Prioritized report directly informs which predicted risks require immediate attention, while the Ransomware Susceptibility report provides specific predictions about ransomware risk, guiding prioritization of remediation efforts. The embedded Knowledgebase provides risk levels, reasoning, recommendations, and reference links to help prioritize security efforts and inform decision-making.

Continuous Monitoring

ThreatNG provides continuous monitoring of an organization's external attack surface, digital risk, and security ratings. This constant nature is fundamental to Predictive Risk Prioritization. Cyber threats are dynamic, and new vulnerabilities or exposures can appear at any time. Continuous monitoring ensures that as the external attack surface changes, new risks are identified immediately, allowing for real-time adjustments to predictive models and defensive strategies. This enables risk prioritization to be constantly updated based on the latest external posture.

Investigation Modules

ThreatNG's investigation modules provide deep dives into various external intelligence aspects, essential for enriching predictive models and understanding potential attack vectors:

• Domain Intelligence:

  • Domain Overview: Provides insights like Microsoft Entra Identification and Bug Bounty Programs. This helps predict how an attacker might gain initial access or identify potential targets for social engineering, allowing an organization to prioritize hardening those areas.

  • DNS Intelligence: Includes Domain Record Analysis (IP Identification, Vendors and Technology Identification), Domain Name Permutations (Taken and Available), and Web3 Domains (Taken and Available). For example, discovering domain name permutations that are "available" helps predict potential squatting or typosquatting attacks for phishing, prioritizing brand protection measures. Identifying specific vendors or technologies via DNS records can predict vulnerabilities associated with those systems, allowing for proactive patching or monitoring.

  • Email Intelligence: Offers Security Presence (DMARC, SPF, and DKIM records) and Harvested Emails. This helps predict the likelihood of email-based attacks, such as spoofing or phishing, by assessing email security controls and identifying exposed email addresses, thereby prioritizing email security enhancements.

  • WHOIS Intelligence: Provides WHOIS Analysis and Other Domains Owned. This can help predict related assets or infrastructure that might be targeted based on ownership patterns, prioritizing a broader security assessment.

  • Subdomain Intelligence: This comprehensive module analyzes HTTP responses, header analysis, server headers (including technologies), cloud hosting, e-commerce platforms, CMS, CRM, email marketing, and many other aspects. It also assesses Subdomain Takeover Susceptibility and identifies content such as admin pages, APIs, Development Environments, VPNs, and exposed Ports (including IoT/OT, ICS, Databases, and Remote Access Services). For Predictive Risk Prioritization, this is invaluable. Suppose it identifies an exposed development environment or a database with known vulnerabilities via an open port. In that case, it directly predicts a high likelihood of compromise through that specific vector, leading to the highest prioritization for remediation of those particular assets. Discovering deprecated headers, for instance, predicts a vulnerability that attackers could use, prioritizing header configuration changes.

  • IP Intelligence: Provides IPs, Shared IPs, ASNs, Country Locations, and Private IPs. This helps predict geographical threat origins or identify shared infrastructure that might increase exposure, allowing for prioritization of network segmentation or geo-blocking.

  • Certificate Intelligence: Covers TLS Certificates (Status, Issuers, Active, Certs without Subdomains, Subdomains without Certificates) and Associated Organizations. Outdated or misconfigured certificates can lead to man-in-the-middle attacks or trust issues, emphasizing the importance of prioritizing certificate management.

• Social Media: ThreatNG analyzes posts from the organization, breaking out the content copy, hashtags, links, and tags. This helps predict reputational risks or potential social engineering targets based on publicly available information, allowing for prioritization of social media monitoring and countermeasures.

• Sensitive Code Exposure: Discovers public code repositories uncovering digital risks that include various access credentials (API Keys, Access Tokens, Generic Credentials, Cloud Credentials), security credentials (cryptographic keys, SSH keys), configuration files, database exposures, application data exposures, activity records, communication platform configurations, development environment configurations, security testing tools, cloud service configurations, remote access credentials, system utilities, and personal data. If ThreatNG finds a hardcoded API key or private SSH key in a public repository, it directly predicts an imminent compromise of systems or data associated with those credentials. This allows the organization to prioritize revoking credentials and securing systems before an attacker can use them.

• Mobile Application Discovery: Discovers mobile apps in marketplaces and their contents, including access credentials, security credentials, and platform-specific identifiers. Similar to code exposure, identifying sensitive data in mobile apps helps predict the direct exploitation of those credentials or keys, thereby prioritizing mobile app security audits.

• Search Engine Exploitation:

  • Website Control Files: Discovers files like robots.txt (identifying secure, user, shopping cart, email, and admin directories, as well as development resources and API directories) and security.txt (revealing contact info, PGP keys, and bug bounty programs). Identifying exposed admin directories via robots.txt can predict an attacker attempting to brute-force or exploit vulnerabilities on those pages, prioritizing access control hardening.

  • Search Engine Attack Surface: Helps investigate susceptibility to exposing errors, general advisories, IoT entities, persistent exploitation, potentially sensitive information, privileged folders, public passwords, susceptible files, susceptible servers, user data, and web servers via search engines. This directly predicts what an attacker can find via simple search engine queries, allowing the organization to prioritize removal of sensitive information from public indexing.

• Cloud and SaaS Exposure: Identifies sanctioned and unsanctioned cloud services, cloud service impersonations, and open exposed cloud buckets (AWS, Azure, and GCP), along with various SaaS implementations. Discovering an unsanctioned cloud service or an open S3 bucket indicates a high risk of data exposure or unauthorized access, necessitating prioritized cloud security posture management.

• Online Sharing Exposure: Identifies the presence of an organizational entity on online code-sharing platforms, such as Pastebin, GitHub Gist, and Scribd. Finding sensitive information on these platforms directly predicts potential data leaks or credential compromise, prioritizing content removal and credential rotation.

• Sentiment and Financials: Includes organization-related lawsuits, layoff chatter, SEC filings of publicly traded US companies, especially their Risk and Oversight Disclosures, SEC Form 8-Ks, and ESG Violations. This helps predict whether an organization might be targeted due to public negative sentiment, financial distress (which could make them a softer target), or specific disclosures that reveal vulnerabilities or risks, thereby prioritizing risk mitigation strategies based on public perception.

• Archived Web Pages: All of the following that have been archived on the organization’s online presence: API, BAK, CSS, Demo Pages, Document Files, Emails, Excel Files, HTML Files, Image Files, Javascript Files, JSON Files, JSP Files, Login Pages, PDF FIles, PHP Files, Potential Redirects, Python Files, Txt Files, XML Files, Directories, Subdomains, User Names, Admin Page. Archived sensitive data can predict future exploitation if attackers find and use outdated credentials or information, emphasizing the importance of prioritizing data clean-up.

• Dark Web Presence: Organizational mentions of Related or Defined People, Places, or Things, Associated Ransomware Events, Associated Compromised Credentials. This directly informs predictions about an organization being targeted for ransomware or other attacks if their credentials or sensitive information are being traded or discussed on the dark web, leading to prioritized credential resets and threat actor monitoring.

• Technology Stack: All of the following technologies being used by the organization under investigation: Accounting Tools, Analytics, API Management, Blogging / Microblogging, Booking, Content Delivery Network or Content Distribution Network (CDN), Content Management Systems (CMS), Customer Relationship Management (CRM), Databases, Developer Platforms, Digital Content Publishing, Ecommerce, Email, Helpdesk Software, Incident Management, Core JavaScript, JavaScript Libraries, JavaScript Frameworks, JavaScript Graphics Libraris, Marketing Automation, Media (Storage, Galleries, Livestreaming), Operating Systems, Point of Sale (POS) / Retail Management, Privacy, Project Management, Security, Shipping, Utilities, Web Servers, Website Development. Knowing the technology stack allows for the prediction of vulnerabilities associated with specific versions or configurations of those technologies, prioritizing patching and secure configuration management.

Intelligence Repositories (DarCache)

ThreatNG’s continuously updated intelligence repositories (DarCache) are critical for robust Predictive Risk Prioritization:

• Dark Web (DarCache Dark Web), Compromised Credentials (DarCache Rupture), and Ransomware Groups and Activities (DarCache Ransomware): Tracking Over 70 Ransomware Gangs. By tracking ransomware gang activity and compromised credentials, ThreatNG can predict if an organization's credentials are for sale or if specific ransomware groups are targeting their industry or geographic location. For example, if "DarCache Rupture" indicates a large number of compromised user credentials for an organization, it predicts a higher likelihood of account takeover attacks, leading to prioritized password resets and the enforcement of multi-factor authentication.

• Vulnerabilities (DarCache Vulnerability): Offers a comprehensive and proactive approach to managing external risks and vulnerabilities by assessing their real-world exploitability, likelihood of exploitation, and potential impact. This understanding enables organizations to make smarter security decisions and allocate resources effectively to protect their digital assets.

  • NVD (DarCache NVD): Includes detailed technical characteristics and potential impact of vulnerabilities (Attack Complexity, Attack Interaction, Attack Vector, Impact scores, CVSS Score, and Severity). This helps predict which technical vulnerabilities are most severe and therefore most likely to be exploited, allowing for prioritized remediation based on severity.

  • EPSS (DarCache EPSS): The data provides a probabilistic estimate of the likelihood of a vulnerability being exploited shortly. Combining the "EPSS" score and "Percentile" with other vulnerability data enables a more forward-looking approach to prioritization, addressing not only severe vulnerabilities but also those that are likely to be weaponized. For example, if a vulnerability has a high CVSS score but a low EPSS, it might be prioritized lower than a medium CVSS with a high EPSS, guiding resource allocation.

  • KEV (DarCache KEV): Lists vulnerabilities that are actively being exploited in the wild with critical context for prioritizing remediation efforts on vulnerabilities that pose an immediate and proven threat. If an organization has a KEV-listed vulnerability, ThreatNG predicts an immediate and proven threat, urging urgent remediation and highest prioritization.

  • Verified Proof-of-Concept (PoC) Exploits (DarCache eXploit): Provides direct links to Proof-of-Concept (PoC) exploits on platforms such as GitHub, referenced by their corresponding CVEs. The presence of a verified PoC significantly increases the predicted likelihood of exploitation, as it demonstrates the practicality of an attack. This information is invaluable for security teams to reproduce the vulnerability, assess its real-world impact on their specific environment, and develop effective mitigation strategies, prioritizing patching efforts for these vulnerabilities.

• ESG Violations (DarCache ESG), SEC Form 8-Ks (DarCache 8-K), and Bank Identification Numbers (DarCache BIN): These help predict non-technical risks that could lead to cyberattacks, such as a company being targeted due to public scandals or financial distress, prioritizing proactive public relations or legal counsel.

• Mobile Apps (DarCache Mobile): Indicates whether Access Credentials, Security Credentials, and Platform-Specific Identifiers are present within the Mobile Apps. This directly predicts the risk of compromise through mobile application weaknesses, leading to prioritized mobile application security audits and code reviews.

Synergies with Complementary Solutions

ThreatNG's external focus and detailed intelligence can be significantly enhanced when combined with other cybersecurity solutions, creating a more comprehensive and robust Predictive Risk Prioritization ecosystem.

• Security Information and Event Management (SIEM) / Security Orchestration, Automation, and Response (SOAR) Platforms: ThreatNG's continuous monitoring and prioritized alerts on external risks can be directly integrated into a SIEM/SOAR platform. For example, if ThreatNG predicts a high likelihood of a subdomain takeover due to specific DNS misconfigurations, a SOAR playbook could be automatically triggered to alert DNS administrators, create a ticket in a vulnerability management system, and even initiate automated checks for the vulnerable configuration. The intelligence from DarCache (e.g., KEV, EPSS) can enrich SIEM alerts, providing crucial context for internal security events. If a SIEM detects suspicious internal network traffic, ThreatNG's data on external attack surface vulnerabilities, dark web compromised credentials, or active ransomware group activities can help confirm or deny an ongoing external attack, thus prioritizing the investigation.

• Vulnerability Management (VM) Solutions: ThreatNG excels at identifying external vulnerabilities and predicting their exploitability through its DarCache Vulnerability data (NVD, EPSS, KEV, PoC Exploits). This intelligence can be fed into traditional VM solutions, which often focus on internal network scans. This synergy ensures that both internal and external vulnerabilities are prioritized based on their actual risk and predicted likelihood of exploitation. For example, suppose ThreatNG identifies an externally exposed vulnerability with a high EPSS score. In that case, the VM solution can prioritize scanning and patching efforts for that specific vulnerability across the internal infrastructure, focusing resources on the most impactful vulnerabilities.

• Endpoint Detection and Response (EDR) Solutions: The predictive insights ThreatNG generates can inform EDR policies and procedures. Suppose ThreatNG predicts an increased risk of a specific type of malware associated with a known vulnerability or ransomware group activity (such as DarCache Ransomware). In that case, EDR solutions can be configured to monitor for those TTPs on endpoints specifically. For instance, if ThreatNG predicts a high likelihood of phishing campaigns targeting an organization's employees (due to BEC susceptibility findings ), the EDR can heighten its detection rules for suspicious email attachments or executable files on endpoints, prioritizing endpoint-level protection.

• Security Awareness Training Platforms: ThreatNG's BEC & Phishing Susceptibility assessment directly identifies an organization's vulnerability to social engineering. This intelligence can be used to tailor security awareness training programs, focusing on the specific phishing tactics or social engineering lures that ThreatNG predicts are most likely to be used against the organization based on its external posture and dark web findings. Suppose ThreatNG identifies a trend of credential stuffing attacks against the organization (as reported by DarCache Rupture). In that case, the training can emphasize strong password practices and multi-factor authentication, prioritizing user education for the most relevant threats.

• Cloud Security Posture Management (CSPM) / Cloud Workload Protection Platform (CWPP): ThreatNG's Cloud and SaaS Exposure findings, particularly identifying open exposed cloud buckets or unsanctioned cloud services, can be directly integrated with CSPM and CWPP solutions. This allows for automated remediation or enhanced monitoring of cloud environments based on external risk predictions. For example, suppose ThreatNG predicts a high risk due to an open S3 bucket. In that case, the CSPM can automatically apply a policy to restrict public access or trigger an alert for immediate review, prioritizing cloud security configuration.

By providing a continuous, attacker-centric view of an organization's external posture, ThreatNG generates the raw material and actionable insights necessary to build a truly predictive risk prioritization program. Its detailed assessments, real-time monitoring, in-depth investigation modules, and rich intelligence repositories collectively enable organizations to anticipate, prioritize, and proactively defend against cyber risks before they become damaging incidents.