Cyber risk prediction in cybersecurity involves forecasting the likelihood of future cyberattacks or security incidents by analyzing historical data, current threat intelligence, and an organization's specific vulnerabilities. It's about proactively identifying potential threats and weaknesses before they can be exploited.

Think of it like weather forecasting. Meteorologists use data about atmospheric conditions, historical patterns, and predictive models to forecast the likelihood of rain, snow, or storms. Similarly, cyber risk prediction uses data about past attacks, emerging threats, and an organization's security posture to anticipate future cyber risks.

Key elements of cyber risk prediction:

• Threat Intelligence: Gathering information about current and emerging cyber threats, including attack methods, vulnerabilities, and threat actors. This information comes from various sources, such as security researchers, government agencies, and commercial threat intelligence providers.

• Vulnerability Assessment: Identifying and assessing an organization's specific vulnerabilities, including weaknesses in systems, applications, and security controls. This involves regular vulnerability scanning, penetration testing, and security audits.

• Historical Data Analysis: Analyzing data from past security incidents, including the types of attacks, the targeted assets, and the impact of the attacks. This helps identify patterns and trends that can inform future predictions.

• Predictive Modeling: Using statistical models and machine learning algorithms to analyze data and generate predictions about future cyber risks. These models can consider various factors, such as the likelihood of specific attack types, the potential impact of attacks, and the effectiveness of security controls.

Benefits of cyber risk prediction:

• Proactive Security: By anticipating future threats, organizations can proactively mitigate risks and strengthen their security posture.

• Prioritization of Resources: Helps organizations focus their limited resources on the most critical risks and vulnerabilities.

• Improved Decision Making: Provides valuable information for making informed security investments and strategies decisions.

• Reduced Impact of Attacks: By predicting and mitigating potential attacks, organizations can reduce the impact of successful breaches.

Examples of cyber risk prediction:

• Predicting the likelihood of a phishing attack based on current trends and the organization's susceptibility to social engineering.

• Forecasting the potential impact of a ransomware attack based on the organization's critical assets and data backup procedures.

• Identifying systems that are most likely to be targeted by specific types of malware based on their vulnerabilities and known attack patterns.

Tools and Technologies:

• Threat Intelligence Platforms (TIPs)

• Security Information and Event Management (SIEM) systems

• Vulnerability Scanners

• Machine Learning algorithms

• Risk Management Software

Cyber risk prediction is an ongoing process that requires continuous monitoring, analysis, and adaptation. Organizations can effectively manage their cyber risks and protect their valuable assets by staying ahead of the curve.

ThreatNG possesses a powerful combination of features contributing significantly to cyber risk prediction. Here's how it achieves this:

1. Threat Intelligence Gathering and Analysis

• Intelligence Repositories: ThreatNG maintains extensive intelligence repositories that include information on:

• Dark web activity: Provides insights into active threat actors, leaked credentials, and planned attacks.

• Compromised credentials: Alerts organizations about exposed employee credentials, enabling proactive password resets and security measures.

• Ransomware events and groups: Tracks ransomware trends, helping predict and mitigate potential attacks.

• Known vulnerabilities: Maintaining an updated database of known vulnerabilities enables proactive patching and mitigation.

• ESG violations: Identifies potential ESG-related risks that could lead to reputational damage or legal action.

• SEC Form 8-Ks: Extracts valuable information from SEC filings, including disclosures of past security incidents, to understand an organization's historical risk profile and predict future vulnerabilities.

• Dark Web Presence Module: Continuously monitors the dark web for mentions of the organization, its employees, or its assets, providing early warnings of potential threats or data breaches.

2. Vulnerability Assessment and Predictive Modeling

• Domain Intelligence Module: Analyzes an organization's domain to identify potential vulnerabilities, such as:

• Exposed APIs: Discovers exposed APIs that attackers could exploit.

• Exposed development environments: Identifies environments that may contain sensitive information or vulnerabilities.

• Known vulnerabilities: Detects known vulnerabilities in web applications and other systems.

• Sensitive Code Exposure Module: Analyzes public code repositories for exposed credentials, API keys, and security misconfigurations, helping predict and prevent potential data breaches.

• Cloud and SaaS Exposure Module: Assesses the security posture of cloud services and SaaS applications, identifying misconfigurations and vulnerabilities that could increase the risk of attacks.

• Sentiment and Financials Module: Analyzes sentiment from news articles, social media, and SEC filings to identify potential reputational risks and financial vulnerabilities that could make the organization a more attractive target for attackers.

• Breach & Ransomware Susceptibility Score: Combines various factors, including domain intelligence, dark web presence, and sentiment analysis, to generate a score that predicts the likelihood of a breach or ransomware attack.

3. Continuous Monitoring and Reporting

• Continuous Monitoring: ThreatNG monitors the organization's attack surface for changes and new threats, providing real-time alerts and updates.

• Reporting: Generates various reports, including security ratings, inventory reports, and ransomware susceptibility reports, to provide insights into the organization's security posture and track changes over time. This historical data can be used to identify trends and predict future risks.

Examples of Cyber Risk Prediction with ThreatNG

• Predicting Phishing Attacks: By analyzing the organization's Dark Web Presence and Sentiment and Financials, ThreatNG can identify if the organization or its employees have been mentioned in data breaches or if negative news exists. This information, combined with Domain Intelligence, can predict the likelihood of phishing attacks targeting employees with compromised credentials or exploiting vulnerabilities in email security.

• Predicting Ransomware Attacks: By analyzing the organization's Breach & Ransomware Susceptibility score, Dark Web Presence, and Intelligence Repositories on ransomware groups, ThreatNG can predict the likelihood of a ransomware attack. This allows the organization to take proactive steps, such as strengthening data backups, implementing multi-factor authentication, and educating employees about ransomware threats.

• Predicting Supply Chain Attacks: By analyzing the organization's Supply Chain & Third-Party Exposure, ThreatNG can identify potential risks associated with third-party vendors and suppliers. This allows the organization to assess the security posture of its supply chain and take steps to mitigate risks, such as requiring vendors to meet specific security standards or implementing security controls to limit access to critical systems.

By combining threat intelligence, vulnerability assessment, continuous monitoring, and predictive modeling, ThreatNG empowers organizations to identify and mitigate cyber risks proactively, strengthening their overall security posture and reducing the likelihood of successful attacks.