A governance-by-design framework for secure ai-enabled digital agriculture platforms

Özet

Digital agriculture platforms now bring together a wide range of data sources, including sensor measurements, satellite imagery, meteorological records, farm-level observations, mobile applications, and AI-based models. This integration creates clear opportunities for precision agriculture, climate-smart production, evidence-informed advisory services, and more sustainable use of agricultural resources. Yet the same platforms also raise difficult questions about data ownership, cybersecurity, model reliability, privacy, and accountability in decision-making. In practice, many digital agriculture projects still give greater attention to predictive performance than to the governance and security structures required for responsible use. This imbalance becomes especially problematic when such systems are expected to inform real decisions that affect farmers, public institutions, production planning, and environmental sustainability.

This study develops a governance-by-design framework for secure AI-enabled digital agriculture platforms. The framework is organized around three mutually reinforcing layers: data governance, cybersecurity and AI risk management, and decision accountability. The data governance layer covers issues such as data ownership, access rights, consent, data quality, provenance, and responsible data sharing. The cybersecurity and AI risk management layer deals with identity management, role-based access control, API security, IoT and sensor vulnerabilities, data poisoning, model manipulation, system logging, and incident monitoring. The decision accountability layer focuses on how AI-generated recommendations should be explained, validated, documented, and linked to human oversight.

The central contribution of this study is its shift in perspective. Rather than treating digital agriculture platforms mainly as model-driven prediction tools, the study frames them as trustworthy decision-support infrastructures. By bringing together information systems governance, cybersecurity controls, and AI accountability principles, the proposed framework offers a conceptual basis for designing agricultural digital platforms that are secure, auditable, and responsibility-aware. In this respect, the framework can support researchers, software developers, agricultural institutions, and policymakers in building more reliable and accountable digital transformation strategies for agriculture.