SALT LAKE CITY — February 20, 2020 — Venafi®, the leading provider of machine identity protection, and AIR Worldwide, an extreme event modeling firm, today announced the results of a report on the economic impact of poorly protected machine identities. According to the report, between $51 billion to $72 billion in losses to the worldwide economy could be eliminated through the proper management and protection of machine identities.
Machines control the flow of all types of sensitive data, help shape innovation and are fundamental to the way all businesses operate. As a result, the way in which they connect and authorize communication makes them a primary security risk for organizations. Cybercriminals routinely target machine identities and their capabilities because they are often poorly protected. Once compromised, machine identities are powerful tools for attackers, allowing them to hide malicious activity, evade security controls and steal a wide range of sensitive data.
Key findings from the Venafi/AIR Worldwide report include:
- Unprotected machine identities account for $15 billion to $21 billion in economic losses in the U.S., or 9% to 13% of total U.S. economic losses due to cyber events, which are estimated at $163 billion
- 14% to 25% of the cyber losses for the largest companies organizations with revenues over $2 billion) are machine identity related. This is in comparison to 6%-16% of cyber losses for smaller companies (organizations with revenue ranges below $2 billion.
“The scope and scale of this project could only be analyzed using the groundbreaking technique of AIR Worldwide, their sophisticated model has revealed the machine identity risks Fortune 500 organizations face today,” said Kevin Bocek, vice president, security strategy and threat intelligence at Venafi. “Unfortunately, many businesses are relying on processes and techniques from over 20 years ago, which poorly protect machine identities and, as AIR Worldwide found, can result in billions of dollars of loses. Digital transformation is dependent on cloud, microservices and APIs, and all of this requires the authentication and privacy that machine identities provide. Cybercriminals understand that breaking this link means hitting the jackpot.”
AIR Worldwide’s estimates were obtained by combining cyber event data sets with assessments of upward of 100,000 firms’ performance in various areas of cybersecurity. It gave security ratings that assessed the management of cybersecurity, such as proper configuration and management of SSL/TLS certificates; user behavior, such as use of file-sharing services and protocols like torrent; and indicators of compromise, such as communications to botnet command and control servers. The firm’s methodology took company size and industry into consideration when calculating economic loss estimates.
Data sources used for the economic estimates included:
- >Event data sets: This data provided a list of publicly reported, historical cyber events, including those that involve breach/data compromise and downtime events. These data sets also indicated the company name, industry sector, event categorization, brief event description and number of records lost for data compromise events.
- Firmographic data sets: This data provides a complete list of U.S. businesses, along with firmographic information about each listed company— including company name, industry sector, employee count and revenue.
- Technographic data sets: This data provided a list of businesses, along with technographic information (i.e., information about used technologies, the cyber supply chain and management of computer assets) about each listed company—including company name, industry sector, employee count and security rating.
“We’re excited to collaborate with Venafi and be a part of this innovative study which evaluates the current cost of machine identity breaches,” said Dr. Eric Dallal, senior scientist at AIR Worldwide. “Estimating the financial impacts of cyber security practices is always a challenging problem, requiring a combination of data, models, and subject matter expertise. We were able to leverage our experience when we developed a model estimating the impact of cyber security practices on data compromise event frequency. The results of this study show that there are very real costs when failing to adequately protect machine identities.”
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