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.

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.

Code leakage was the most common type of sensitive data sent to GenAI tools.

535 separate incidents of sensitive exposure were recorded involving Chinese GenAI tools.

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.

More than half (53%) of organisations that have implemented Privileged Access Management (PAM) reported improved protection of sensitive data.

85% of organisations say at least 40% of their cloud data is sensitive.

Over half of cloud data is now classified as sensitive.

40% of healthcare leaders say protecting patient data is a significant challenge.

More than 1 in 4 healthcare organizations reported that at least half of their sensitive patient data was at risk due to cyberattacks.

Almost 40% of organizations admit they lack the tools to protect AI-accessible data.

36% of healthcare leaders admit their current cybersecurity tools cannot protect cloud-based patient data.

18% of enterprise PCs store sensitive data.

HR and employee records account for 4.8% of sensitive data going into AI.

Sales and marketing data constitutes 10.7% of sensitive data going into AI.

R&D materials account for 17.1% of sensitive data going into AI.

Currently, 34.8% of all corporate data that employees input into AI tools is classified as sensitive. This is a substantial increase from 27.4% a year ago and more than triple the 10.7% observed two years ago.

Source code is the most common type of sensitive data employees put into AI, accounting for 18.7% of sensitive data.

Health records comprise 7.4% of sensitive data going into AI.

22% of respondents stated concern about the loss of sensitive data due to insecure home networks.