26.3% of ChatGPT use by employees was via personal accounts.

2.1% of all sensitive prompts analysed in Q2 originated in Poe.

68% of CISOs consider supply chain risk and generative AI security to be top concerns, viewing them as intertwined challenges that are redefining the attack surface.

The average enterprise uploaded 1.32GB of files (half of which were PDFs) to GenAI tools and AI-enabled SaaS applications in Q2. A full 21.86% of these files contained sensitive data.

Of analyzed prompts and files submitted to 300 GenAI tools and AI-enabled SaaS applications between April and June, 22% of files (totaling 4,400 files) and 4.37% of prompts (totaling 43,700 prompts) were found to contain sensitive information.

Sensitive data in files sent to GenAI tools showed a disproportionate concentration of sensitive and strategic content compared to prompt data, with files being the source of 79.7% of all stored credit card exposures, 75.3% of customer profile leaks, 68.8% of employee PII incidents, and ◦ 52.6% of total exposure volume in financial projections.

Java was found to be the riskiest language for AI code generation, with a security failure rate over 70%. Other major languages, such as Python, C#, and JavaScript, presented significant risk, with failure rates between 38 percent and 45 percent.

When given a choice between a secure and insecure method to write code, GenAI models chose the insecure option 45% of the time.

LLMs failed to secure code against log injection (CWE-117) in 88% of cases

AI-generated code introduces security vulnerabilities in 45% of cases.

In 45% of all test cases, LLMs introduced vulnerabilities classified within the OWASP Top 10.

LLMs failed to secure code against cross-site scripting (CWE-80) in 86% of cases.

Financial information accounted for 14.4% of sensitive data exposed through employee use of Chinese GenAI tools at work.

Customer data represented 12.0% of sensitive data exposed through employee use of Chinese GenAI tools at work.

Legal documents made up 4.9% of sensitive data exposed through employee use of Chinese GenAI tools at work.

Mergers & acquisitions data accounted for 18.2% of sensitive data exposed through employee use of Chinese GenAI tools at work.

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

Personally identifiable information (PII) comprised 17.8% of sensitive data exposed through employee use of Chinese GenAI tools at work.

The majority of sensitive data exposure (roughly 85%) due to the use of Chinese GenAI tools occurred via DeepSeek, followed by Moonshot Kimi, Qwen, Baidu Chat and Manus.

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