Predictive Threat Intelligence
Predictive Threat Intelligence (PTI) in cybersecurity is an advanced, proactive approach that focuses on forecasting and identifying potential cyber threats before they materialize into actual attacks. Unlike traditional threat intelligence, which often reacts to incidents that have already occurred or are currently happening, PTI aims to anticipate future threats by analyzing historical data, current trends, and the behaviors of cyber adversaries.
Here's a detailed breakdown of Predictive Threat Intelligence:
How it Works:
Predictive Threat Intelligence uses a sophisticated combination of technologies and methodologies to achieve its proactive stance:
Extensive Data Collection: PTI systems gather vast amounts of data from diverse sources, including:
Internal Security Logs: Network logs, firewall logs, endpoint activity logs, user behavior logs.
External Threat Intelligence Feeds: Commercial and open-source feeds providing information on known Indicators of Compromise (IoCs) like malicious IP addresses, domains, and file hashes.
Open-Source Intelligence (OSINT): Publicly available information such as news articles, security research, social media, and technical forums.
Deep and Dark Web Monitoring: Scrutinizing underground forums, marketplaces, and chat groups where cybercriminals plan and execute attacks, share tools, and leak data.
Human Intelligence (HUMINT): Information gathered from human sources, though less common in automated PTI systems.
Advanced Analytics and Processing: The collected raw data is then processed and analyzed to extract meaningful insights. This involves:
Normalization: Standardizing data formats from various sources for consistent analysis.
Correlation: Identifying relationships and connections between seemingly disparate data points to uncover patterns.
Contextualization: Adding context to the data to understand its relevance and potential impact on a specific organization.
AI and Machine Learning (ML): This is the core of PTI's predictive capability. AI and ML algorithms are applied to the processed data to:
Identify Patterns and Trends: Detect recurring attack patterns, emerging threat actor Tactics, Techniques, and Procedures (TTPs), and shifts in the cyber threat landscape.
Behavioral Analytics: Establish baselines of regular user and system behavior, and then flag deviations that could indicate malicious activity. This helps detect both known and unknown threats.
Predict Future Attacks: Based on identified patterns and trends, AI/ML models can forecast the likelihood of specific attack types, the vulnerabilities most likely to be exploited, and even the probable next steps of threat actors. For example, if a specific ransomware group is increasingly targeting a particular industry, PTI can alert organizations in that industry to prepare.
Threat Modeling: Create detailed profiles of potential threats and identify attack chains.
Actionable Insight Generation and Dissemination: The ultimate goal is to transform raw data and analytical findings into practical, actionable intelligence. This intelligence is then disseminated to relevant stakeholders (security teams, leadership) in a digestible format. This can include:
Prioritized alerts on critical threats.
Recommendations for patching specific vulnerabilities.
Guidance for updating defense strategies.
Insights for strategic decision-making regarding cybersecurity investments.
Key Components:
While the process outlines how it works, the key components that make up a PTI system include:
Data Aggregation Platforms: Tools and systems to collect and centralize data from diverse sources.
Big Data Analytics Engines: Capable of processing and analyzing massive datasets.
Machine Learning and AI Models: Algorithms designed for pattern recognition, anomaly detection, and predictive modeling (e.g., supervised, unsupervised, deep learning).
Threat Intelligence Platforms (TIPs): Software platforms that integrate, process, and enrich threat data, often providing visualization and reporting capabilities.
Vulnerability Management Integration: Connecting predictive insights with vulnerability scanning and patching efforts.
Incident Response Integration: Enabling faster and more effective response by providing advanced warning and context.
Benefits of Predictive Threat Intelligence:
Proactive Defense: The most significant benefit is the shift from a reactive to a proactive security posture, allowing organizations to prevent attacks rather than respond to them.
Early Detection: Identify threats at their nascent stages, minimizing potential damage and downtime.
Reduced Risk: Mitigate the likelihood and impact of successful cyberattacks.
Improved Incident Response: Advanced warning enables security teams to develop and rehearse incident response plans for specific anticipated threats, leading to faster and more efficient resolution.
Optimized Resource Allocation: Focus security efforts and resources on the most probable and impactful threats, rather than spreading them thin.
Enhanced Decision-Making: Provide data-driven insights for strategic cybersecurity investments and policy development.
Detection of Unknown Threats: By analyzing behaviors and patterns, PTI can identify novel or zero-day threats that traditional signature-based detection methods might miss.
Cost Efficiency: Preventing breaches is generally far less expensive than recovering from them.
In essence, Predictive Threat Intelligence serves as an early warning system, enabling organizations to anticipate and mitigate cyber threats before they can cause significant harm.
ThreatNG, an all-in-one external attack surface management, digital risk protection, and security ratings solution, offers comprehensive capabilities that directly contribute to practical Predictive Threat Intelligence. It helps organizations anticipate and mitigate threats by focusing on external, unauthenticated perspectives, much like an attacker would.
ThreatNG's Role in Predictive Threat Intelligence
ThreatNG’s approach to external discovery and assessment provides the foundational data and insights necessary for anticipating future attacks. 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 Threat Intelligence as it mirrors an attacker's initial reconnaissance efforts. By identifying an organization's internet-facing assets from an outside-in perspective, ThreatNG helps predict 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 identify and exploit. This discovery process provides the initial dataset for predicting where vulnerabilities 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 Threat Intelligence, this means foreseeing which web applications are most likely to be targeted for hijacking attempts, allowing an organization to strengthen those defenses 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 remediate before an attack occurs.
BEC & Phishing Susceptibility: Derived from sentiment, financial findings, domain intelligence (including DNS permutations and Web3 domains), and email intelligence (security presence and format prediction), as well as dark web presence of compromised credentials. 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 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 analysis, financial data (including lawsuits, SEC filings, and negative news), and domain intelligence. ThreatNG helps predict potential brand-damaging events by identifying public negative sentiment or legal issues that cybercriminals might leverage in extortion or reputational attacks.
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. 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.
Cyber Risk Exposure: This considers parameters like 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.
ESG Exposure: ThreatNG rates organizations based on discovered environmental, social, and governance (ESG) violations through its external attack surface and digital risk intelligence, highlighting areas like competition, consumer, employment, environment, financial, government contracting, healthcare, and safety-related offenses. This can predict the likelihood of activist-driven cyberattacks or reputational damage based on an organization's public ESG footprint.
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 use, 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.
Breach & Ransomware Susceptibility: This assessment uses external attack surface and digital risk intelligence, including domain intelligence (exposed sensitive ports, private IPs, and vulnerabilities), dark web presence (compromised credentials, ransomware events, and gang activity), and sentiment and financial data (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 implementation of specific defensive measures.
Mobile App Exposure: ThreatNG assesses the exposure of an organization’s mobile apps by identifying them in marketplaces and verifying for sensitive content, including 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.
Positive Security Indicators: This unique feature identifies and highlights an organization's security strengths, such as Web Application Firewalls or multi-factor authentication, validating their effectiveness from an external attacker's perspective. For Predictive Threat Intelligence, 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.
Reporting
ThreatNG provides various reports, including Executive, Technical, Prioritized (High, Medium, Low, 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 Threat Intelligence as they translate complex assessment data into actionable insights for different stakeholders. For example, the Prioritized report directly informs which predicted threats require immediate attention, while the Ransomware Susceptibility report provides specific predictions about ransomware risk.
Continuous Monitoring
ThreatNG offers continuous monitoring of external attack surface, digital risk, and security ratings for all organizations. This constant nature is fundamental to Predictive Threat Intelligence. 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 predictions 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 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.
DNS Intelligence: Includes Domain Record Analysis (IPs, Vendors, Technologies), 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. Identifying specific vendors or technologies via DNS records can predict vulnerabilities associated with those systems.
Email Intelligence: Offers Security Presence (DMARC, SPF, 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.
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.
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, and exposed Ports (including IoT/OT, ICS, Databases, and Remote Access Services). For Predictive Threat Intelligence, this is invaluable. If it identifies an exposed development environment or a database with known vulnerabilities via an open port, it directly predicts a high likelihood of compromise through that specific vector. Discovering deprecated headers, for instance, predicts a vulnerability that attackers could use.
IP Intelligence: Provides IPs, Shared IPs, ASNs, Country Locations, and Private IPs. This helps predict the geographical origins of threats or identify shared infrastructure that may increase exposure.
Certificate Intelligence: Covers TLS Certificates (Status, Issuers, Active, Certs without Subdomains, Subdomains without Certificates) and Associated Organizations. Outdated or misconfigured certificates can predict man-in-the-middle attacks or trust issues.
Social Media: ThreatNG analyzes posts from the organization, breaking out content copy, hashtags, links, and tags. This helps predict reputational risks or potential social engineering targets based on publicly available information.
Sensitive Code Exposure: Discovers public code repositories and uncovers digital risks including 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 revoke credentials and secure 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, finding sensitive data in mobile apps helps predict direct exploitation of those credentials or keys.
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) andsecurity.txt(revealing contact info, PGP keys, and bug bounty programs). Identifying exposed admin directories viarobots.txtcan predict an attacker attempting to brute-force or exploit vulnerabilities on those pages.Search Engine Attack Surface: Helps investigate susceptibility to exposing errors, general advisories, IoT entities, persistent exploitation, potential 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 remove 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, GCP), along with various SaaS implementations, such as Salesforce, Slack, Splunk, and ServiceNow. Discovering an unsanctioned cloud service or an open S3 bucket predicts a high risk of data exposure or unauthorized access.
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.
Sentiment and Financials: Includes organization-related lawsuits, layoff chatter, SEC filings, SEC Form 8-Ks, and ESG Violations. This helps predict whether an organization might be targeted due to public negative sentiment, financial distress (which might make them a softer target), or specific disclosures that reveal vulnerabilities or risk.
Archived Web Pages: Identifies archived content like APIs, login pages, document files, emails, and usernames. Archived sensitive data can predict future exploitation if attackers find and use outdated credentials or information.
Dark Web Presence: Mentions of related people, places, or things, associated ransomware events, and 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.
Technology Stack: Identifies all technologies used by the organization, including CRM, databases, web servers, and operating systems. Knowing the technology stack allows for the prediction of vulnerabilities associated with specific versions or configurations of those technologies.
Intelligence Repositories (DarCache)
ThreatNG’s continuously updated intelligence repositories (DarCache) are critical for robust Predictive Threat Intelligence:
Dark Web (DarCache Dark Web), Compromised Credentials (DarCache Rupture), and Ransomware Groups and Activities (DarCache Ransomware): By tracking over 70 ransomware gangs 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.
Vulnerabilities (DarCache Vulnerability): This repository provides a comprehensive and proactive approach to managing external risks, considering the real-world exploitability, likelihood of exploitation, and potential impact.
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.
EPSS (DarCache EPSS): Offers a probabilistic estimate of the likelihood of a vulnerability being exploited soon. This is a direct predictive metric. Combining EPSS with severity allows ThreatNG to predict which vulnerabilities are not only severe but also have a high probability of being weaponized soon, guiding proactive patching efforts. 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.
KEV (DarCache KEV): Lists vulnerabilities actively being exploited in the wild, providing critical context for prioritizing remediation. If an organization has a KEV-listed vulnerability, ThreatNG predicts an immediate and proven threat, urging urgent remediation.
Verified Proof-of-Concept (PoC) Exploits (DarCache eXploit): Provides direct links to PoC exploits on platforms like GitHub, referenced by CVE. The presence of a verified PoC significantly increases the predicted likelihood of exploitation, as it demonstrates the practicality of an attack. Security teams can use this to reproduce and test the vulnerability, thereby prioritizing patching efforts.
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.
Mobile Apps (DarCache Mobile): This highlights the presence of exposed access credentials, security credentials, and platform-specific identifiers within discovered mobile apps. This directly predicts the risk of compromise through mobile application weaknesses.
Synergies with Complementary Solutions
ThreatNG's external focus and detailed intelligence can be significantly enhanced when used with other cybersecurity solutions, creating a more holistic and robust Predictive Threat Intelligence 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.
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.
Endpoint Detection and Response (EDR) Solutions: While ThreatNG focuses on external threats, the predictive insights it 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 (from 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.
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. If ThreatNG identifies a trend of credential stuffing attacks against the organization (from DarCache Rupture), the training can emphasize the importance of strong password practices and multi-factor authentication.
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.
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 threat intelligence 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 threats before they become damaging incidents.
