AI Data Exposure Hits 1 in 6 Supply Chain Firms, Survey Shows

A new survey reveals that approximately 17 percent of supply chain, logistics, distribution, and wholesale professionals have experienced data exposure incidents involving AI tools in the past year. The research, conducted by InstaLILY and fielded through Pollfish, surveyed 250 managers and executives and found that 78 percent of respondents operate AI systems with access to sensitive customer pricing and margins, while 67 percent allow access to supplier pricing and rebate data. This widespread exposure creates significant risk despite high confidence levels: 87 percent of respondents expressed confidence in their AI safeguards, yet 63 percent acknowledged data overlap between supplier and customer information across their platforms. The survey results highlight a fundamental tension in how supply chain organizations deploy AI technology.

While nearly 9 in 10 respondents indicated they would be more likely to select AI tools specifically designed for distribution if stronger data controls were available, current obstacles remain substantial. The most cited barrier (22 percent of respondents) is that existing AI tools simply were not architected with data separation requirements in mind. Cost and technical expertise gaps compound the problem, cited by 21 percent and 20 percent of respondents respectively. These findings align with broader cybersecurity concerns: IBM's 2025 Cost of a Data Breach report documented that 13 percent of organizations experienced breaches involving AI models, with 97 percent of those lacking proper AI access controls.

The operational implications are significant for supply chain leaders. Trading partners increasingly question how vendors protect sensitive information, with 63 percent of survey respondents reporting such inquiries and 30 percent acknowledging these concerns influenced vendor selection decisions. Supply chain teams must now treat data governance as a critical evaluation criterion when selecting AI platforms, not an afterthought. The survey recommends stress-testing AI systems against routine scenarios, such as verifying whether a tool can generate customer quotes without exposing confidential supplier terms, and establishing clear approval workflows before AI systems access sensitive information.