Webinar Explains How Dark-Web Data Can Inform Cybersecurity Risk Models
Summary
A DarkOwl webinar explains how dark-web data, including exposed credentials and ransomware leaks, could supplement cybersecurity risk models. It discusses data limitations, supply-chain exposure, and ways organizations might assess risk.
Key points
- The speaker describes deep- and dark-web sources that may contain exposed credentials, corporate information, ransomware chatter, and leaked data.
- The presentation argues that dark-web exposure can add a data point to risk models, while noting its scoring approach is not an absolute risk metric.
- The speaker says DarkOwl’s experimental model predicted 73% of breaches and all four ransomware cases it analyzed; these are vendor-reported claims.
- The webinar uses supply-chain examples involving Volvo and Okta to emphasize monitoring vendors as well as an organization’s own exposure.
- The speaker recommends assessing what data is leaked, considering external influences, and regularly evaluating whether risk models are effective.
- The presentation advocates for shared methods and a common language for quantifying dark-web data in cybersecurity risk modeling.
Article Details
- Topic
- How deep and dark web data can inform cybersecurity risk modeling, with ransomware as a case study
People
Threat Actors
ALPHVRansomware group identified as having increased activity.ContiRansomware group discussed in connection with data leaks and activity around the Ukraine conflict; the source says it largely disbanded into splinter groups.CoomingNamed in the article’s discussion of ransomware-related data available after the invasion of Ukraine.LockBitRansomware group identified as having increased activity.SnatchRansomware group identified as having increased activity and discussed in connection with a breach in Volvo’s supply chain.StormousNamed in the article’s discussion of ransomware-related data available after the invasion of Ukraine.
Vendors
Products
Industries
CybersecurityOf the numerous quantitative models that attempt to define and quantify the cybersecurity risk to organizations, very few consider risk indicators from the deep and dark web.InsuranceAll aspects of insurance underwriting and cyber security assessments need to be worried about risk modeling.