On 5th February, EFFoST as a partner in the TITAN EU project, participated in the “Using AI for Food Risk Prevention” workshop organized by Agroknow, in the framework of the EFRA project. The event, which also featured other EU projects such as HOLiFOOD and ECO-Ready, showcased how artificial intelligence (AI) and data-driven approaches are revolutionising food safety management.
Presentations from experts Bas van der Velden (Wageningen UR), Giannis Stoitsis (Agroknow), and Ali Hürriyetoğlu (Wageningen UR) illustrated how AI-powered tools are enhancing risk assessment, improving prediction accuracy, and strengthening food safety protocols. AI-powered tools allow companies to anticipate hazards before they escalate into crises. By analysing historical data, environmental factors, and real-time industry trends, AI can detect contamination risks, food fraud, and supply chain vulnerabilities with greater efficiency. One example highlighted was the case of ethylene oxide contamination. Initially undetected, its discovery underscored the challenge of addressing ”unknown unknowns” risks—those that industries don’t even realise exist. AI has the potential to bridge this gap by continuously scanning data sources to identify emerging threats.
The hands-on session allowed participants to explore AI-powered risk forecasting dashboards and their practical applications in Food Safety and Quality Assurance (FSQA) workflows. One significant takeaway was the potential of AI tools in monitoring geopolitical shifts and climate-related disruptions that impact food supply chains. For example, in the aftermath of the war in Ukraine, AI could have helped companies assess risks, predict fraud potential, and ensure the safe sourcing of ingredients such as soy lecithin.
The workshop also highlighted the crucial role of data sharing in food risk prevention. Catarina Petisca (Puratos), during the panel discussion, pointed out the challenge posed by data silos within the food industry. Despite the vast amounts of data that companies collect, their reluctance to share this information—due to privacy concerns, regulatory barriers, and competition—limits the industry’s ability to effectively respond to emerging risks. Experts proposed solutions such as anonymised data aggregation and blockchain-based platforms, which could foster secure, collaborative data sharing. They argued that standardised data collection and real-time monitoring would greatly enhance early warning systems for food borne illnesses and vulnerabilities within supply chains. Additionally, the panel discussion discussed about the legal and ethical implications of using AI and data. While AI’s predictive capabilities offer clear advantages, concerns over bias, transparency, and regulatory compliance persist. Panellists underscored the need for harmonized industry standards and regulatory frameworks to ensure that AI tools remain both effective and trustworthy.
The event concluded with a shared recognition of AI’s transformative potential in food safety. While AI-driven tools and predictive analytics are still in their early stages of adoption, their rapid growth signals a future in which the food industry transitions from reactive to proactive risk management.
The key takeaway? By embracing AI and secure data-sharing mechanisms, the food industry can better anticipate risks, improve safety protocols, and ultimately ensure safer products. As the industry continues to refine AI algorithms and foster collaboration, the potential to enhance food safety on a global scale is immense.
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