The future of digital innovation in transforming food safety systems in the developing world

Abstract

Low- and middle-income countries bear the most significant burden of foodborne diseases, impacting their food and nutrition security, trade, and ultimately economic growth. Recent advances in digitization and artificial intelligence provide new opportunities to transform food safety systems, addressing inefficiencies through better oversight and improved decision-making. This article synthesizes current practices and developments related to food safety and digital innovation, and proposes a Digital Food Safety Transformation Framework.

Introduction: underdevelopfood safety systems in LMICs?

According to the 2025 State of Food Security and Nutrition in the World report, about 673 million people faced hunger and 2.3 billion people experienced moderate or severe food insecurity in 2024¹. As part of the food security and nutrition challenge, Africa and South‑East Asia carry disproportionate foodborne illness and mortality, particularly among children under five². These estimates further underscore the urgent need to modernize food safety systems and ultimately transform the agrifood system. Still, low- and middle-income countries (LMICs) bear the burden of underdeveloped systems, including fragmented oversight, limited surveillance, a lack of risk-based principles, and the absence of internationally recognized food safety services (e.g., certification and laboratory testing), as well as extensive informal food economies. Digital technologies have begun to penetrate the work of food safety stakeholders; nevertheless, there remains room to identify best practices, particularly in the context of current realities in industrializing countries.

Methodology

This article presents a qualitative and integrative review of current practices, challenges, and opportunities in the digital transformation of food safety systems in low- and middle-income countries (LMICs). Based on the existing sources, including recent reports and case studies from LMICs and developed economies, the review identifies structural constraints and proposes a tailored framework for digital transformation. The methodology includes:

• Systematic literature review of peer-reviewed articles, policy documents, and technical guidelines.

• Expert synthesis of insights from the Vienna Food Safety Forum 2025 (VFSF 2025), a global multi-stakeholder dialog.

• Comparative analysis of digital tools and implementation modalities across different country contexts.

• Framework development based on thematic clustering of challenges and solutions, resulting in a seven-layer Digital Food Safety Transformation Framework.

• Risk typology construction for digital and AI deployment, informed by ISO standards and Codex guidelines.

This approach ensures that the proposed framework is grounded in both scientific evidence and the practical realities of LMICs.

Many review articles have proposed potential digital solutions and provided a catalog of digital and AI tools for food safety^1,3–6. Some of these solutions are still not widely used by food safety competent authorities or the private sector, partly because they do not adequately address existing constraints. In addition, no established framework for digital transformation in food safety that connects foundational principles in food safety, consideration of circumstances for digitalization, and good governance practices. It is vital to emphasize that digital solutions should be adjusted to the actual needs of beneficiaries; nevertheless, some of these tools are designed for more advanced food safety systems. This review addresses these gaps by proposing a comprehensive framework that integrates solutions and implementation modalities. To ensure trustworthiness of these solutions, the proposed framework will be founded on Codex, Food and Agriculture Organization (FAO), World Health Organization (WHO) guidance, GS1 standards, and International Organization for Standardization (ISO) standards.

LMIC realities—And how the transformation addresses them

Infrastructure and connectivity—In many LMICs, unreliable electricity and patchy broadband are structural constraints that slow the rollout of digital solutions. Even though the International Telecommunication Union (ITU) reported continued growth in Internet use, universal, affordable connectivity, particularly in rural areas, remains a challenge in low-income regions, and 5G population coverage illustrates stark inequities (approximately 84% in high-income countries versus around 4% in low-income countries)^7,8. In 2023, 92% of the global population had access to electricity, leaving approximately 666 million people without it. Approximately 85% of that population resides in Sub-Saharan Africa⁹. Internet use continues to grow, yet gaps persist, particularly due to affordability, limited rural access, and inadequate advanced mobile coverage¹⁰. To address these challenges, designed responses to digital food safety solutions should include offline-first mobile apps with local caching, edge AI for anomaly detection, and power-aware Internet of Things (IoT) gateways. Electronic Product Code Information Services (EPCIS) messages reduce the need for high-bandwidth by using event-based monitoring, simple short message service (SMS), and unstructured supplementary service data (USSD) channels to back up reporting in the event of data network failures.

Fragmentation and the informal market—A significant fraction of food in LMICs is traded through informal markets, where food safety challenges are characterized by limited surveillance, outdated food processing and storage practices, and inadequate infrastructure (e.g., safe water, electricity, processing facilities, and storage). Analyses suggest the informal sector is a significant enabler for foodborne risk and is often overlooked by development initiatives focused on formal processors and exporters^11–13. Informal markets are also usually linked to rural areas where structural constraints further exacerbate the possibility of deploying innovative digital tools developed to empower stakeholders (food business operators and competent authorities) for the promotion of improved food safety practices. Due to the informal setting and limited information (data), local banks will not be able to provide credit to these food business operators. Offline/EDGE capability, integrated with market intelligence (e.g., price) and a buyer matchmaker, will further encourage these communities to transition to formal markets.

Technology adoption costs—for micro-, small-, and medium-sized enterprises (MSMEs)- are the backbone of emerging economies. Still, they often operate at low productivity and thin profit margins, leaving them with limited budgets for compliance technology¹⁴. This occurs at a time when the European Union (EU) and other major jurisdictions are introducing additional regulations, such as the EU Corporate Sustainability Due Diligence Directive and the EU Deforestation Regulation, thereby increasing compliance costs. Shared utilities, such as cooperative‑level data platforms with hosted traceability, can lower the total cost of ownership. The cost of developing relevant platforms, e.g., performance management systems for inspectors of competent authorities, can be substantially reduced by specifying business requirements, considering these standards, and recruiting local programmers to develop these solutions. This would also ensure the long-term sustainability of these systems as development know-how would remain with the end user.

Data readiness, tackling fragmentation, trust, and sovereignty—Siloed, paper-based, or incompatible datasets limit analytics and cross-agency collaboration in many LMICs. In addition, international standards will remain a crucial factor in data readiness, thereby ensuring credibility and trustworthiness. For instance, FAO and WHO promote risk-analysis approaches that depend on relevant local data; however, LMICs frequently lack standardized, shareable datasets due to the absence of harmonized standards, joint platforms, and internationally recognized conformity assessment services, particularly in areas such as inspection and laboratory testing^15,16. From an industry perspective, the adoption of the GS1 EPCIS standard and controlled vocabularies for batch, movement, and condition events will allow following a standardized system. Furthermore, the use of federated learning models can support AI in training locally and sharing model updates, rather than raw data, thereby respecting privacy and sovereignty while enabling collaboration^17,18.

Interoperability and vendor lock‑in—Closed systems lead to long-term dependency and high switching costs. Many equipment manufacturers have ensured brand loyalty by using software that is incompatible with other devices and interfaces. Guidance on digital government procurement recommends considering interoperability in life-cycle costing (Most Economically Advantageous Tender (MEAT) criteria) and favoring open standards and open application programming interfaces (APIs) to minimize lock-in^18,19. As a practical step, stakeholders can incorporate conformity standards from industry (e.g., EPCIS) and international standards (e.g., UNCEFACT, Codex data model for certificates) as a requirement in tenders and grants to develop solutions²⁰.

Capability, data literacy, and culture—Transformation is both organizational and technical. To ensure adequate user practices, as suggested during the VFSF 2025, the national food safety curriculum should be expanded and include modules on artificial intelligence and machine learning, which were previously labeled as advanced statistics and computation. As part of the transformation, good practices in change management, particularly in the development of internal capacity-building programs, will be crucial to upskill organizations’ workforces. To ensure continuous monitoring and improvement, particularly in data quality, privacy, and collaboration, organizations must consider establishing a dedicated unit that will work closely with the organization’s operational and strategic decision-making arms.

What we learned from the Vienna Food Safety Forum 2025

The Vienna Food Safety Forum 2025 (VFSF 2025) was organized as a global dialog to share emerging solutions on digitalization from all stakeholders (public and private sector, academia, development partners, and food safety service providers) and support the work of regulators, particularly those in developing countries, who consider the uptake of digital solutions. More specifically, its primary purpose was to demonstrate how digital technologies and data-driven tools can be integrated into food safety systems to support LMICs’ endeavors²¹. Among many topics, the forum highlighted the practical value of risk prioritization using data analytics, drawing on structured experiences from food authorities and FAO/WHO guidance to optimize inspection resources. It also showcased the application of AI and machine learning to import screening and horizon scanning, such as the United States Food and Drug Administration’s (FDA) Predictive Risk-based Evaluation for Dynamic Import Compliance Targeting (PREDICT) system, next-generation decision support efforts, and industry platforms that surface emerging hazards earlier. As part of the topics, traceability and data standards featured prominently, for instance, the adoption of industry standards, illustrating how they enable targeted recalls and supply chain transparency for actors such as retailers or MSMEs^20,22.

Participants reviewed the trade facilitation benefits of electronic certification, concluding that it reduces clearance times and curbs fraud. They also discussed the use of remote audits, aligned with Codex CXG 102-2023 and ASEAN practices, to maintain oversight under resource constraints^3,22–24. Collectively, VFSF 2025 reinforced the principle that digital transformation contributes to risk-based food safety systems that protect consumers and enable fair trade²¹. Decision-makers emphasized the need for a call to action to leverage digital innovation to improve food safety systems, particularly in LMICs. During the discussions, it was noted that more guidance will be required to support the integration of digital solutions in food control systems. Decision-makers emphasized the need for a call to action to use these solutions to improve food safety systems.

An LMIC‑ready digital food safety transformation framework

With the proliferation of digital innovation, there is an increasing need to establish a framework to help LMICs use these solutions effectively. This seven-layer framework approach is proposed to provide a conceptual basis for the design and deployment of development tools and techniques, as well as digital solutions (see the summary of the framework in Table 1). The proposed framework serves as a decision-support mechanism for policy-makers during the adoption of digital technologies.

Table 1.

Digital food safety transformation framework for LMICs

Framework layer	Description
1. Food safety foundations	Risk analysis, Codex principles, international standards (Codex, WOAH, IPPC), performance monitoring framework
2. Enablers	Offline/edge capability, mobile-first interfaces, power-aware deployments, GS1 traceability
3. Digital services offered by competent authorities	Electronic certification, remote audit, IoT-based infrastructure, database, alert networks, and systems
4. Risk assessment and monitoring of digital technologies	Typology of risks related to the use of digital solutions, digital risk register, monitoring, and mitigation plans
5. Financing and procurement	Lifecycle costing, MEAT criteria, open standards, donor-backed shared utilities
6. Data collaboration and trust	Data sharing between the public and private sectors, a trusted data space, and alert networks
7. Capability and change management	Competency frameworks, training, organizational change, and continuous improvement

The table above illustrates the seven-layer framework for digital food safety transformation, designed explicitly for LMICs. Each layer addresses specific challenges and potential implementation modalities. The layer labeled “food safety foundations” should serve as a first step to ensure compliance with fundamental principles. The remaining layers can be used interdependently and in a sequence that is best fit for policy-makers. Accordingly, they can be further adjusted, if needed, and expanded in line with the mandate of the competent authority, considering the deployment of digital solutions in their operations.

(i) Food safety foundations: Food safety functions and services based on international standards (Codex, World Organization of Animal Health (WOAH), International Plant Protection Convention (IPPC)) are integral parts of food safety. Harmonization of food safety standards plays a crucial role in ensuring trustworthiness and equal measures by food safety competent authorities. Recent Codex guidelines (CXG 93‑2021, CXG 38-2021, and CXG 102-2023) also offer guidance to competent authorities on how to modernize their food control systems. In addition, multiple tools, such as FAO’s Food Control System Assessment Tool, have been developed to identify potential gaps in food safety systems. Creating a better understanding of the possible gaps in national food control systems should be a prerequisite to deploying digital solutions.

(ii) Enablers: Considering LMIC-specific limitations for the broader use of digital tools, several technological solutions could be introduced depending on the needs: offline/edge capability, mobile‑first interfaces in local languages, power‑aware deployments, minimal data models and ontologies. In addition, event-based traceability, using the GS1 standard and barcodes, can reduce bandwidth usage and improve interoperability²⁰.

(ii) Digital services overseen by competent authorities: Competent authorities will provide certain digital products which food businesses or other competent authorities might use: electronic/paperless certification, remote audit and inspection, agricultural management information management system hosted through shared infrastructure, distributed ledgers (where it adds value for stakeholders)^25–27.

(iv) Risk assessment and monitoring of digital technologies: Assessing the potential risks of deploying digital/AI solutions and monitoring their impact should be a good regulatory practice that governs digital transformation. This has to be coupled with a digital risk register to monitor potential risks linked to the use of digital solutions (bias/drift, security, over‑automation/over‑reliance, and multi‑agent miscoordination) and also mitigation measures (audit trails, fallback plans, explainability)^28,29.

(v) Financing and procurement: Government or official development assistance-funded projects need to set principles related to the financing and procurement of digital solutions. Adopting life-cycle costing MEAT criteria that help secure technological solutions based on open standards, interoperability, and exit rights will help avoid vendor lock-in. Consider donor-backed shared utilities (e.g., hosted traceability) and sustainability strategies for competent authorities during the planning phase to improve^19,30.

(vi) Data collaboration and trust: Promote trust-based data spaces through cooperation among stakeholders, defining relevant protocols, regulations, and rights to use. Where international standards exist, such as the use of credible industry assurance data to reduce duplicative controls and electronic certification to scale trusted official certificate exchanges, competent authorities must consider becoming early adopters to familiarize themselves with data-sharing practices^31,32. At a regional level, shared data hubs, alert networks, and risk‑assessment services can be potential areas for collaboration. These ideas, for instance, are also exemplified by the Africa Food Safety Agency (AfFSA) adopted by the African Union in 2025 and its planned continental data hub and rapid alert system^33–35.

(vii) Capability and change management: Ultimately, these digital solutions will be used and monitored by inspectors, risk assessors, policy-makers, and laboratory staff. Expanding the current competency frameworks and developing internal capacity-building programs in collaboration with UN entities, such as the United Nations Industrial Development Organization (UNIDO), the FAO, or the WHO, will help food safety competent authorities implement international guidelines and standards^36,37. Organizational structure needs to reflect the new decision-making processes and continuous improvement and monitoring of digital solutions.

From tasks to technology: prioritizing solutions based on needs

As scientific evidence-based risk analysis practices are fundamental principles of food safety^15,16, the same principles should be applied when using emerging and potentially disruptive technologies, such as machine learning or AI. Before their deployment, LMICs would have to prioritize the digital solutions based on their capabilities and the specific needs of competent authorities. As part of that, they have to assess (the potential positive and negative) impact, considering the need, ability to operate, costs, and the time of implementation, and self-assessment tools developed by UN bodies can play a crucial role in assisting in this process. In addition, well-documented case studies will help competent authorities in better understanding the transformation process and the envisaged challenges.

Today, solutions should be opportunity-driven to fulfill their purpose. This will help improve the capacity and efficiency of food safety surveillance. Beforehand, competent authorities must develop performance-monitoring frameworks with baseline data to measure progress. It is also worth noting that as food control systems transition to a modernizing system, more gaps and food safety issues will be identified, which might also pose challenges for policy-makers.

Based on these points, a couple of examples are explained below, which can be scaled globally, including among LMICs.

Better-targeted inspections and enforcement: As part of their inspection regime, decision-makers can prioritize higher-risk establishments and products using historical non-compliance data, complaint records, and signals of trend change. Data generated by audits against voluntary third-party assurance (vTPA) programs can be used by competent authorities in accordance with Codex guideline CXG 93-2021³⁸. As another example, the Singapore Food Agency has implemented targeted, data-driven operations, illustrating improved hit rates^39,40. Overall, WHO’s 2024 practical guide provides models for risk‑based inspection frequency and prioritization as a crucial step before integrating external data for risk profiling of various sectors⁴¹.

Risk prioritization for border control and assurance: In a globalized food trade system, export/import control is a central part of food regulators, where large amounts of data can be used to build predictive models, thereby optimizing border inspection practices based on risk, e.g., the FDA’s PREDICT system^42,43. On the industry side, large manufacturers deploy real‑time horizon scanning systems and commercial platforms like Horizon Scan, which aggregate more than global sources with thousands of new reports every month to flag emerging hazards for prioritization of quality assurance^23,44.

Remote inspection and authenticity testing: Remote audit and inspection practice became more widespread as a result of COVID-19 measures on social distancing. As auditors and inspectors were unable to visit food businesses, several new methods were piloted during this period, and best practices were captured in the relevant Codex guideline²⁵. In addition, technical advancements in the use of sensors and data transmitters offer new ways in traceability. A compact electronic nose in the form of spectral devices paired with supervised learning and computer vision can support on‑site classification and pathogen detection⁴⁵. The Joint IAEA-FAO Center can help countries deploy advanced testing methods, such as stable isotope fingerprints and related profiling, to authenticate origins in commodities like coffee and dairy, thereby deterring fraud^24,46,47.

Paperless certification for trade facilitation: The revision of Codex CXG 38‑2021 contributed to the paperless or electronic exchange of official certificates through a data model following UNCEFACT standards⁴⁸. Regarding phytosanitary certification, the ePhyto solution hosted by IPPC has scaled across developing and developed countries, exchanging more than 100,000 phytosanitary certificates per month, with documented benefits in security, clearance time, and fraud reduction⁴⁹. Where necessary, due to a high level of food trade, LMICs should consider developing single-window systems interoperable with other systems that allow the exchange of electronic certification. During the planning phase, they need to consider regional priorities and existing endeavors to avoid duplication and prioritize harmonization at a regional level.

Generative AI (GenAI) and multi‑agent systems. Recent developments in GenAI and multi-agent systems have prompted consideration of how they could be used for more complex tasks associated with food regulatory practices. A symposium titled “Data Readiness on Artificial Intelligence,” hosted by the European Food Safety Authority (EFSA) and its Advisory Group on Data (AGoD) in October 2024, has looked into opportunities, current practices, and challenges for AI. In addition, this event focused on exploring the possibilities and requirements for implementing Artificial Intelligence (AI) within the food safety ecosystem⁵⁰. The symposium offered attendees the chance to discuss AI applications, hear from experts such as the FDA about their AI programs, and collaborate on innovative data and AI solutions⁵¹. This shows that forming technical working groups at the regional level can help countries exchange knowledge and experience, potentially leading to improvements in their own systems.

Risk assessment for the deployment of digital solutions and AI

The integration of digital and AI technologies into food safety systems necessitates a systematic approach to assessing the risks associated with their use. The ultimate purpose of using AI should be to assist stakeholders in making better-informed decisions, not to replace any human judgment. With the increased deployment of AI, practical experience-based expertise will enable us to validate and control the accuracy of AI recommendations.

Authorities should maintain a living risk register that spans model-related risks, such as drift, bias, and explainability gaps. Operational risks can arise from over-reliance on automation, security and privacy threats, coordination failures in multi-agent systems, and questions about the reliability of evidence gathered through remote audits. Mitigation should combine continuous performance monitoring, human-in-the-loop thresholds, privacy-enhancing technologies, and simulation testing of agent interactions, alongside strengthened standard operating procedures for the collection and verification of remote evidence^17,18,52. Good governance practices are anchored in Codex texts (CXG 62-2007, CXG 82-2013, CXG 91-2017) and the FAO/WHO risk analysis framework, ensuring alignment with international standards while enabling responsible use of innovative technologies. Table 2 provides a typology of various risks that can emerge with the deployment of digital and AI solutions in food safety systems.

Table 2.

Typology of risks for digitalization and AI deployment

Risk	Mitigation strategy
Bias and drift	Continuous performance monitoring, human-in-the-loop thresholds
Security and privacy	Privacy-enhancing technologies, secure data protocols
Over-reliance on recommendations and automation	Fallback plans, human oversight and decisions, and explainability mechanisms
Multi-agent miscoordination	Simulation testing, governance protocols
Remote audit reliability	Standard operating procedures, evidence verification

The table presents a risk register for digital and AI deployment in food safety systems, highlighting key risks and mitigation strategies.

Recent ISO standards (ISO/IEC 23894:2023 – Information technology—Artificial intelligence — Guidance on risk management and ISO/IEC 42001:2023 – Information technology — Artificial intelligence — Management system) provide structured guidance for AI risk management. ISO/IEC 23894:2023 outlines a comprehensive process including risk identification, analysis, evaluation, treatment, monitoring, and communication. On the other hand, ISO/IEC 42001:2023 complements this by embedding risk assessment into an AI management system, emphasizing lifecycle oversight, transparency, and continuous improvement. These standards offer a globally recognized foundation for integrating AI governance into food safety systems^29,53.

A forward-looking research and evaluation agenda is needed to guide evidence-based decisions in food safety and assist LMICs in adopting digital technologies. Comparative studies should measure outcomes from risk-based inspection, such as cost-effectiveness and improved surveillance and oversight, as well as the effects of electronic certification on border clearance and fraud detection, and the contribution of traceability to narrowing the scope of recalls in LMIC contexts. Further research in behavioral science should assess how AI assistants influence inspector performance and accountability, and identify safeguards to mitigate automation bias and prevent surprises. Technical work on federated learning models should compare their accuracy and resource demands with those of centralized models. Exploring privacy–utility trade-offs and the computing constraints typical of LMIC settings will help ensure compliance with data regulations and standards. Interventions in informal markets require rigorous documentation and evaluation of impact, as well as incentives that support pathways to formalization. Finally, regional public goods, such as regional or continental data hubs and rapid alert systems, should be designed in consideration with other initiatives, thereby reducing cost implications and improving uptake and the likelihood of cross-border spillovers.

Conclusions

The transformation of food safety systems in developing countries is already underway, and the adoption of digital technologies is playing an increasingly important role in this process. With the right combination of digital innovation, scientific evidence-based risk analysis principles, and good regulatory practices, LMICs can address constraints in food safety systems and build more resilient, risk-based systems that protect consumers and enable fair trade. The proposed Digital Food Safety Transformation Framework, coupled with the risk assessment practices presented in this review, offers a practical decision-support mechanism for food safety regulators in deploying digital technologies in their processes. The framework encourages developing digital solutions for real-world constraints, adopting open standards, aligning technologies with food safety tasks, and embedding responsible AI practices from the beginning, enabling better-informed decision-making for food safety regulators. The proposed framework requires practical applications and documentation of recommendations, including potential gaps and recommendations, for future applications and additional improvements. Such articles could also serve as case studies for policy-makers and users of the framework.

By investing in shared data infrastructure and food safety services, as well as in regional collaboration, LMICs would accelerate progress toward safer food systems and inclusive economic growth. The future of food safety is digital, and LMICs should benefit equally and be part of this transformation to ensure a food-secure future.

Author contributions

G.M. conceptualized this review and developed the Digital Food Safety Transformation Framework based on its previous research and experience in international development. He conducted the literature review, synthesized insights from the Vienna Food Safety Forum 2025, and integrated lessons learned in the proposed framework. He designed the risk typology for digital and AI deployment and formulated the research and evaluation agenda. The author was solely responsible for writing, editing, and finalizing the manuscript.

Data availability

The author may provide any data or information upon reasonable request.

Competing interests

The authors declare no competing interests.