The average small healthcare employee has access to more than 5,500 sensitive files.

28% of the workforce have admitted to using AI to access sensitive data.

57% of employees input sensitive data into free-tier AI 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.

Of these incidents involving Chinese GenAI tools, the exposed data types included: 32.8% involving source code, access credentials, or proprietary algorithms; 18.2% including M&A documents and investment models; 17.8% exposing PII such as customer or employee records; and 14.4% containing internal financial 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.

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.

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

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

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.