Why Food Companies Don't Share Food Safety Data, and What Would Change It

Picture a model that could tell a processing plant which days next week carry elevated contamination risk, before a single lab result comes back. Or one that could flag an emerging hazard across a dozen facilities in three states while it is still a pattern rather than an outbreak.

The data to train that model already exists. It sits in the testing records of the more than 40,000 food and beverage processing facilities operating in the United States. Very little of it is ever shared beyond the company that collected it.

The food safety data bottleneck

AIFS supports research that makes this kind of data collection and modeling possible. In 2025, AIFS-supported researchers at Cornell published one answer: a public repository of benchmark food safety datasets that machine learning researchers could actually build on.

That raised the next question. If you build the place where shared food safety data could live, why would any company put its data there?

This June, an AIFS-supported study in npj Science of Food, “Perceptions of voluntary horizontal confidential food safety data sharing: an exploratory interview study with food industry leadership,” set out to answer it by asking the people who make the decision.

What 27 industry leaders said

Linda Kalunga, Renata Ivanek and colleagues interviewed 27 leaders across the food industry about voluntary, confidential, horizontal data sharing. Horizontal means sharing between companies at the same point in the supply chain, which is to say, between competitors.

Four themes emerged.

1. They already see the benefits. Companies are not failing to understand the value of shared data. Participants described collective gains, like stronger surveillance and earlier detection, alongside gains to their own firms.
2. Much of the data isn’t in shareable shape. Limited digitalization is a real constraint. Many food safety records still live in formats that were never designed to leave the building.
3. Trust decides the outcome. Trust in industry peers, regulators, customers, and in the protection measures themselves. Where trust was absent, no technical assurance replaced it.
4. Companies are asking for governance. The recurring concern was loss of control once data leaves the organization.

Why food safety data sharing stalls

Two frameworks explain the pattern. Collective Action Theory asks why organizations fail to cooperate even when the shared benefit is obvious. Coopetition Theory examines the strain on firms that must cooperate and compete at once. Together they point to three mechanisms:

• Uneven cost-benefit distributions. The parties who gain most from a shared dataset are often not the ones who bear the most cost and risks in contributing to it.
• Opportunism concerns. A company that shares in good faith must consider what a competitor, a customer, or a plaintiff’s attorney could do with the same data.
• Participation cost asymmetries. A large processor with a data infrastructure team and a small processor with a filing cabinet face the same request at wildly different real costs.

None of this is solved by a better algorithm. The study concludes that meaningful food safety data sharing requires governance structures that change the incentives firms respond to. Technology is downstream of that.

Why these matters

On September 1, 2026, USDA announced a plan to modernize agricultural data collection, built on four pillars. Two are the subject of this research: expanding secure data sharing and evaluating AI applications and protecting producer privacy while explaining clearly how data is used.

This research explains why participation stays thin when the incentive to contribute is weaker than the risk of contributing, however strong the privacy protections. The barrier was never only exposure. It was who absorbs the cost and who captures the benefit.

What comes next: privacy-preserving methods

The same AIFS team at Cornell is now working on the other half of the question. If a company is willing to share, what technical approach protects it?

That work compares anonymization, differential privacy, and federated learning across food safety datasets of different sizes and structures, measuring what each method costs in predictive accuracy. Early indications are that no single approach wins across the board, and that the right choice depends on the shape of the data. Those results are still in preparation and have not been peer reviewed.

Together, the two halves suggest a workable system: governance that makes participation rational, and methods that make it safe.

About the study

Kalunga, L., Koebel, K., Alexander, C., Wiedmann, M., Smith, A., Adalja, A., & Ivanek, R. (2026). Perceptions of voluntary horizontal confidential food safety data sharing: an exploratory interview study with food industry leadership. npj Science of Food. https://doi.org/10.1038/s41538-026-00939-9

This work was supported by the AI Institute for Next Generation Food Systems with the support of USDA-NIFA award #2020-67021-32855, the Cornell Institute for Digital Agriculture Research Innovation Fund, and NIH award T32OD011000.