Background
The global dairy industry faces persistent and evolving threats from milk adulteration and fraudulent practices, which jeopardize public health, economic stability, and consumer trust. Conventional detection methods are reactive and increasingly inadequate, necessitating a paradigm shift towards preemptive prevention.
Scope and approach
This review critically examines the potential of a cyber-secure AI ecosystem, an integrated framework that progressively fuses Artificial Intelligence (AI) and cybersecurity to transform the dairy supply chain through seamless technical integration. We analyze how AI-driven methodologies, such as machine learning (ML) for non-linear pattern recognition, spectroscopic analysis for chemical profiling, and computer vision (CV) for visual inspection, are unified with cybersecurity measures via specific interfaces like secure Application Programming Interfaces (APIs) for Internet of Things (IoT) data ingestion and blockchain smart contracts for data validation. This enables encrypted data flows from supply chain sensors (capturing temperature, geolocation, and compositional profiles) to AI models, with feedback mechanisms, such as predictive anomaly alerts and adaptive model retraining, ensuring continuous system resilience and end-to-end data integrity, moving beyond isolated technologies to a cohesive ecosystem.
Key findings and conclusions
Our synthesis demonstrates that this deep integration creates a robust, multi-layered defense system where AI's predictive analytics are fortified by cybersecurity's protective protocols, enabling forensic attribution through immutable audit trails and a decisive transition from detection to prevention. For example, real-time IoT data, secured by zero-trust architectures, feeds into AI models for adulterant detection, with feedback loops triggering automated responses like data quarantines or model updates via federated learning, ensuring dynamic system adaptability. However, this paradigm shift faces significant hurdles, including the black box nature of complex AI models, the cost and scalability of blockchain, and socio-technical adoption barriers. We conclude that the strategic implementation of this ecosystem, while not a panacea, is critical for safeguarding public health and ensuring dairy industry integrity. The review provides a cohesive roadmap for stakeholders, emphasizing collaborative governance, standardized protocols, and emerging technologies like federated learning and quantum-resistant encryption to overcome existing barriers and build a resilient supply chain.
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