In 2023, the global cyber insurance market was valued at approximately $13 billion, reflecting a significant increase from $7 billion in 2020. This growth underscores the urgent need for businesses to transfer the risk of cyber-attacks to insurers, especially as the market is projected to reach around $29 billion by 2027.

Despite this growing demand for insurance products, insurers face unique challenges in assessing risks. Unlike traditional insurance, which relies on historical data for reliable assessments, the dynamic nature of cyber threats complicates predictions. Additionally, as companies adopt new technologies and services, their digital footprints continuously change, often leaving them unaware of all the assets that need protection.

Cyber insurance firms rely on cybersecurity tools to evaluate security controls, but these tools often use passive OSINT data, leading to inaccuracies. The rise of AI adds further complexity, highlighting the need for effective navigation of these challenges.

Cybersecurity Risk Assessment Challenges

Cybersecurity underwriting presents distinct challenges. While historical data on security incidents exists, predicting future risks is complicated by several factors:

• Dynamic Threats
  

Unlike predictable risks in fields like medicine or auto insurance, cyber threats are constantly evolving. Cybercriminals adapt their Tactics, Techniques, and Procedures (TTPs), making it hard to rely solely on past incidents for predictions. What may be a known attack vector today can become a novel risk tomorrow.

• Complex Threats
  

The core of CyberMindr’s solution includes an extensive library of nearly 16,000 attack scripts, which can be automatically executed via a multi-stage attack and validation engine. This granular library enables the combination of scripts into complex, multi-step attacks, facilitating the evaluation of intricate threats and obscure risks often missed by other solutions.

• Changing Assets
  

When conducting automated risk assessments, CyberMindr employs a zero-knowledge, unbiased discovery approach to identify all assets linked to a target company, without relying on the company’s own knowledge of its assets. While other solutions depend on OSINT data, which can be inaccurate, CyberMindr’s method mitigates the false-negative and false-positive problems.

As part of the discovery process, CyberMindr’s multi-stage validation engine cleans the data, ensuring only validated findings are presented. This effectively addresses the industry’s challenges with false positives and negatives, providing a more accurate inventory of assets.

• Unknown Unknowns​
  

CyberMindr’s zero-knowledge, unbiased asset discovery approach excels at identifying unknown unknowns. By casting a wide net across diverse data sources, it gathers extensive information, which is then refined through a validation engine that cleans up errant data before it’s surfaced.

The solution also conducts active scanning of assets to uncover additional details about software and services, crucial for accurate risk evaluation. With a deep understanding of asset provisioning and common setup errors, CyberMindr employs a predictive engine to enhance its discovery workflow.

• Lack of Standards​​
  

CyberMindr approaches risk evaluation by leveraging years of cybersecurity expertise and best practices from various frameworks, including NIST, CIS, and MITRE ATT&CK. While a universally accepted set of standards does not exist, CyberMindr measures risk against established security concepts, ensuring a comprehensive evaluation across different standards.

Current Tools for Evaluating Cybersecurity Risks

Various methods are used to identify cybersecurity risks, each with its advantages and disadvantages:

• Questionnaires and Surveys: Gather information about a client’s cybersecurity posture.
• Penetration Testing: Simulates a cyberattack to uncover vulnerabilities.
• Incident Analysis: Reviews past cyber incidents within the industry to identify common risks.
• Policy Review: Assesses a client’s cybersecurity policies and procedures.
• Cybersecurity Risk Scoring: These tools aggregate data from threat intelligence, public databases, and open-source information to calculate a risk score, helping organizations gauge their overall cybersecurity risk.
• External Attack Surface Mapping: Scans the internet for an organization’s publicly exposed assets, such as IP addresses and domains, to identify potential vulnerabilities and enhance security measures.

Limitations of Current Risk Evaluation Tools

Questionnaires and Surveys

• Dynamic Threat Landscape: Assesses readiness for known threats but not emerging ones.
• Complex Threat Vectors: Reveals handling of complex threats but can’t predict new ones.
• Rapidly Changing Assets: Provides understanding but fails to predict unknown risks.
• Unknown Unknowns: Offers insight into handling unknown risks but can’t foresee them.
• Limited effectiveness due to reliance on historical data and self-reporting.
• Complex Threat Vectors: Struggle to capture multi-stage attacks.
• Rapidly Changing Assets: Fail to account for real-time asset changes.
• Unknown Unknowns: Cannot identify unknown risks.

Penetration Testing

• Dynamic Threat Landscape: Useful for current vulnerabilities but may miss emerging TTPs.
• Complex Threat Vectors: Identifies individual vulnerabilities but may overlook complex threats.
• Rapidly Changing Assets: Snapshot-based, not reflecting the current asset state.
• Unknown Unknowns: Identifies some unknown risks but is limited to tested areas.

Analysis of Past Incidents

• Dynamic Threat Landscape: Informs about common risks, but new threats may not be included. Complex Threat Vectors: Offers insights but cannot predict future, unseen attacks.
• Rapidly Changing Assets: Lacks insight into risks from rapidly changing assets.
• Unknown Unknowns: Does not address unknown risks.

Review of Policies and Procedures

• Dynamic Threat Landscape: Assesses readiness for known threats but not emerging ones.
• Complex Threat Vectors: Reveals handling of complex threats but can’t predict new ones.
• Rapidly Changing Assets: Provides understanding but fails to predict unknown risks.
• Unknown Unknowns: Offers insight into handling unknown risks but can’t foresee them.

Cybersecurity Risk Scoring

• Dynamic Threat Landscape: Provides a broad view but may miss organization-specific risks.
• Complex Threat Vectors: Limited in identifying complex threats unique to an organization.
• Rapidly Changing Assets: May not capture real-time asset changes.
• Unknown Unknowns: Helps uncover some unknown risks, but not all.

External Attack Surface Mapping

• Dynamic Threat Landscape: Identifies exposed assets but not emerging TTPs.
• Complex Threat Vectors: Limited in identifying threats involving internal assets or non-technical risks.
• Rapidly Changing Assets: Useful for external mapping but doesn’t cover internal assets.
• Unknown Unknowns: May reveal some unknown risks externally but not those related to internal factors.

Threat Intelligence

• Dynamic Threat Landscape: Offers insights into emerging threats but may overlook organization-specific vulnerabilities.
• Complex Threat Vectors: Provides general information on complex threats but lacks the granularity to identify tailored, multi-stage attacks.
• Rapidly Changing Assets: Primarily focuses on organizational threats and is not designed for asset or vulnerability discovery.
• Unknown Unknowns: Not equipped for identifying unknown risks related to assets or vulnerabilities.

Dark Web Monitoring

• Dynamic Threat Landscape: Effective for identifying threats in obscure areas but less likely to surface specific security issues for the organization.
• Complex Threat Vectors: Not focused on threat vectors or TTPs; instead, it identifies threat actors targeting the organization or reselling data/IP.
• Rapidly Changing Assets: Not designed for asset or vulnerability discovery.
• Unknown Unknowns: Lacks capabilities for discovering unknown risks related to assets or vulnerabilities.

Introducing CyberMindr: A New Solution for Cybersecurity Risk Assessment

To address the limitations of current tools for quantifying cybersecurity risk, CyberMindr has emerged as a fully automated, cloud-based platform designed to map and validate multi-stage attack vectors. It provides insurance companies with an efficient tool for assessing cybersecurity risks during the underwriting process.

With over 15,000 live checks on discovered assets and continuous updates from new playbooks, CyberMindr stays ahead of emerging threats. Its intelligence gathering from monitoring 300+ hacker forums offers insights into the latest Tactics, Techniques, and Procedures (TTPs), enabling insurers to prioritize risks and make informed underwriting decisions based on real-time data.

CyberMindr is an award-winning solution that requires no agents or access permissions, delivering an external view akin to a hacker’s perspective. It conducts real-time monitoring and comprehensive threat exposure assessments with near-zero false positives, ensuring a more accurate risk assessment process.

Developed by expert red teamers and bug bounty hunters, CyberMindr focuses on validated vulnerabilities and confirmed attack paths, providing reliable and actionable data. Unlike traditional ASM tools, it actively scans public-facing assets—such as websites, servers, and applications—identifying only exploitable vulnerabilities. This method minimizes outdated data and false positives.

Key Features of CyberMindr

• Proprietary Prediction Engine: Identifies assets not available through OSINT sources, enhancing risk assessment comprehensiveness.
• Multi-Stage Attack Engine: Continuously updated with new checks to reflect evolving TTPs, keeping insurers informed of current risks.
• Validation Engine: Patent-pending technology confirms assets stealthily, ensuring accuracy without triggering security defenses.

CyberMindr empowers insurance companies to assess cybersecurity risks efficiently and accurately, enhancing the underwriting process and enabling precise policy pricing.

How CyberMindr Addresses Challenges in Cyber Risk Identification

Dynamic Threat Landscape

CyberMindr is built on a dynamic knowledgebase that monitors current and emerging Tactics, Techniques, and Procedures (TTPs) through hacker networks and forums. This allows it to identify risks associated with new threats as they emerge.

Threat Complexity

The core of CyberMindr’s solution includes an extensive library of nearly 16,000 attack scripts, which can be automatically executed via a multi-stage attack and validation engine. This granular library enables the combination of scripts into complex, multi-step attacks, facilitating the evaluation of intricate threats and obscure risks often missed by other solutions.

Changing Asset Inventories

When conducting automated risk assessments, CyberMindr employs a zero-knowledge, unbiased discovery approach to identify all assets linked to a target company, without relying on the company’s own knowledge of its assets. While other solutions depend on OSINT data, which can be inaccurate, CyberMindr’s method mitigates the false-negative and false-positive problems.

As part of the discovery process, CyberMindr’s multi-stage validation engine cleans the data, ensuring only validated findings are presented. This effectively addresses the industry’s challenges with false positives and negatives, providing a more accurate inventory of assets.

Unknown Unknowns

CyberMindr’s zero-knowledge, unbiased asset discovery approach excels at identifying unknown unknowns. By casting a wide net across diverse data sources, it gathers extensive information, which is then refined through a validation engine that cleans up errant data before it’s surfaced.

The solution also conducts active scanning of assets to uncover additional details about software and services, crucial for accurate risk evaluation. With a deep understanding of asset provisioning and common setup errors, CyberMindr employs a predictive engine to enhance its discovery workflow.

Lack of Standards

CyberMindr approaches risk evaluation by leveraging years of cybersecurity expertise and best practices from various frameworks, including NIST, CIS, and MITRE ATT&CK. While a universally accepted set of standards does not exist, CyberMindr measures risk against established security concepts, ensuring a comprehensive evaluation across different standards.

Conclusion: In the rapidly evolving cybersecurity landscape, traditional risk assessment methods struggle to accurately predict and manage complex threats. This whitepaper highlights the challenges of assessing cybersecurity risks—such as the unpredictable tactics of cybercriminals, changing digital assets, and elusive unknown unknowns—underscoring the need for more sophisticated tools. CyberMindr represents a significant advancement in addressing these challenges. By providing real-time, validated insights into vulnerabilities and attack vectors, it enables insurers to make informed underwriting decisions, enhancing both risk assessment precision and underwriting efficiency. As cyber threats continue to increase in frequency and complexity, adopting innovative tools like CyberMindr is essential for insurers to stay ahead. By leveraging cutting-edge technology and continuously updated intelligence, CyberMindr offers a reliable framework for assessing cybersecurity risks, helping insurers protect their clients in an uncertain digital world.