AI-Driven Cybersecurity in Smart Agriculture: Threats, Challenges, and Secure System Architectures

Author: Chintan Hirpara,Khushi Kundariya,Mahipal Chothani,Deep Solanki,Jigar Gajjar
Published Online: July 1, 2026
DOI: http://doi.org/10.63766/spujstmr.26.000090
Abstract
References

Farming has changed dramatically over the past two decades. What once relied on a farmer's experience and physical labour now runs on sensors, AI algorithms, and cloud platforms — and that shift, while genuinely productive, has dragged agriculture into a cybersecurity threat landscape it was never designed to handle. IoT devices sitting in open fields, wireless networks spanning rural distances, and AI models quietly steering irrigation and harvest decisions each represent exploitable entry points for motivated attackers. Worse, most farmers lack cybersecurity training, devices run on default credentials, and no universal security standard exists for agricultural technology. This paper addresses that gap through three contributions: a STRIDE-based threat analysis mapping adversarial risks to specific farm system components; a five-layer Security-by-Design architectural framework integrating AI-driven defences from physical surveillance through to supply-chain traceability; and algorithm-level specifications for the AI mechanisms within that framework. Cybersecurity in agriculture is no longer optional — it is a national food security requirement.

Keywords: Smart Agriculture, Cybersecurity, IoT, AI, STRIDE, Security-by-Design, Food Security, Machine Learning.
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