New research shows that 71.7 percent of workplace AI tools are high or critical risk, with 39.5 percent inadvertently exposing user interaction/training data and 34.4 percent exposing user data.
The analysis from Cyberhaven draws on the actual AI usage patterns of seven million workers, providing an unprecedented view into the adoption patterns and security implications of AI in the corporate environment.
The study finds 83.8 percent of enterprise data is going to risky AI tools, instead of to enterprise-ready tools which present low or very low risks.
“Since the launch of ChatGPT, AI adoption has become one of the fastest growing workplace technologies in history. What began as individual experimentation with generative AI has rapidly evolved into essential business tools across organizations of all sizes — an unprecedented shift from novelty to necessity,” says Nishant Doshi, chief product and development officer at Cyberhaven. “Our research reveals that as this trend continues, organizations face unprecedented data security challenges and points to the fact that companies need to harness AI’s transformative potential while protecting their most valuable information assets.”
An increasing amount of sensitive information is at risk of being exposed, 34.8 percent of corporate data employees put into AI tools is sensitive, up from 27.4 percent a year ago and 10.7 percent two years ago.
The most common types of sensitive data employees put into AI are source code (18.7 percent of sensitive data), R&D materials (17.1 percent) and sales and marketing data (10.7 percent).
AI adoption is highest among younger, mid-level employees. Staff like analysts and specialists use AI tools 3.5 times as much as the next-nearest cohort (manager-level employees). Among software engineers, the highest AI usage is among mid-level employees, who use AI 189 percent more than their more junior counterparts.
You can read more and get the full report on the Cyberhaven blog.
Image credit: Retrosesos/Dreamstime.com
#tools #present #critical #data #risks #enterprise
