According to Venafi cybercriminals routinely target machine identities and their capabilities because they are often poorly protected, and 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”.
Venafi and AIR Worldwide, an extreme event modelling firm, have just announced the results of a report on the economic impact of poorly protected machine identities.
Key findings from the Venafi/AIR Worldwide report include:
- Unprotected machine identities account for US $15 billion to US $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 US $163 billion.
- 14% to 25% of the cyber losses for the largest companies (organisations with revenues over $2 billion) are machine identity related. This is in comparison to 6%-16% of cyber losses for smaller companies (organisations with revenue ranges below $2 billion).
“The scope and scale of this project could only be analysed using the ground-breaking technique of AIR Worldwide, their sophisticated model has revealed the machine identity risks Fortune 500 organisations 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.”
“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.”