Among the 1,059 users who engaged with Chinese GenAI tools, there were 535 incidents of sensitive data exposure.

Code and development artifacts made up 32.8% of sensitive data exposed through employee use of Chinese GenAI tools at work.

45% of cybersecurity practitioners expressed concern about near-term operational genAI risks such as inaccurate outputs.

33% of respondents are still not conducting regular security assessments, including penetration testing, for their Large Language Model (LLM) deployments.

68% of cybersecurity practitioners expressed concern about long-term genAI threats like adversarial attacks.

76% of security leaders (C-suite and VP level) are more concerned about long-term genAI threats like adversarial attacks.

32% of LLM pentest findings are serious

Overall, 69% of serious findings across all pentest categories are resolved.

The resolution rate for high-severity vulnerabilities found in LLM pentests falls to just 21%.

48% of security leaders believe a “strategic pause” is needed to recalibrate defenses against genAI-driven threats.

72% of security leaders cite genAI-related attacks as their top IT risk.

36% of security leaders and practitioners admit that generative AI (genAI) is moving faster than their teams can manage.

36% of security leaders expressed concern about near-term operational genAI risks such as inaccurate outputs.

50% of respondents want more transparency from software suppliers about how they detect and prevent vulnerabilities.

46% of all survey respondents are concerned about sensitive information disclosure due to genAI.

42% of all survey respondents are concerned about genAI model poisoning or theft.

37% of all survey respondents are concerned about genAI training data leakage.

Only 28% of transportation and logistics organizations have begun implementing solutions to mitigate GenAI network and security challenges.

56% of transportation and logistics organizations are either still evaluating GenAI risks or are unprepared for them.

28% of healthcare executives say they are likely to invest in generative AI for social engineering attacks.