Cybersecurity's Crystal Ball: Predicting Risks with Peer Data
"Unlock the secrets to stronger cybersecurity by learning how industry benchmarks and collaborative data sharing are revolutionizing risk prediction."
For years, organizations have grappled with two fundamental cybersecurity questions: What's our true risk exposure, and how do our defenses stack up against others? Historically, the data needed to answer these questions—security posture details, incident reports, and financial losses—was too sensitive to share. The advent of privacy-enhancing technologies (PETs) is changing the game, enabling secure computation of aggregate cyber risk metrics without revealing sensitive, individual data.
The ability to benchmark cyber posture against peers and estimate risk within specific economic sectors is now within reach. Recent research introduces a framework that uses industry-wide data, securely computed, to give organizations a clearer picture of their cyber risk landscape. The core innovation is the "Defense Gap Index," a measure of the weighted security gap between an organization and its peers, forecasting security risk based on historical industry data.
This approach has been applied in a specific sector, using data from 25 large firms in partnership with an industry Information Sharing and Analysis Organization (ISAO). The resulting industry risk model provides participants with tools to estimate their risk exposure and confidentially compare their security posture against their peers, promising a more secure and resilient future.
Decoding the Defense Gap Index: A New Metric for Cyber Risk

The "Defense Gap Index" is a critical component of this new framework. It quantifies the disparity between an organization's security measures and the average security posture of its peers. By securely aggregating data on security controls, incident frequencies, and financial losses, the index provides a benchmark for assessing relative risk.
- Secure Data Aggregation: Privacy-enhancing technologies (PETs) securely compile data on security posture, control failures, incident rates, and losses from participating organizations.
- Weighted Security Posture Deviations: The index calculates how an organization's security controls deviate from the peer group average, weighting these deviations based on the financial losses attributed to specific control failures. Controls that, when failed, led to larger losses have a greater impact on the index.
- Risk Forecasting: The index uses historical industry data to forecast an organization's security risk based on its Defense Gap Index score. This allows firms to empirically predict future risk, supporting investment decisions and helping regulators set reasonable security expectations.
The Future of Cyber Risk Modeling: Collaborative, Data-Driven, and Secure
The research highlights the potential for secure, collaborative approaches to revolutionize cyber risk management. By leveraging privacy-enhancing technologies and industry-wide data, organizations can gain unprecedented insights into their risk profiles and benchmark their security posture against their peers. As governments and industry groups promote data sharing and standardization, the future of cybersecurity will be more data-driven, proactive, and resilient.