Precision agriculture uses a variety of embedded and connected technologies that rely on remote sensing, global positioning systems, and communication systems to generate big data, data analytics, and machine learning. These technologies allow for more precise application of agricultural and livestock management inputs such as fertilizer, seeds, and pesticides, resulting in lower costs and improved yields. A consequence of this rapidly advancing digital revolution is the increased exposure to cyber and other vulnerabilities to the agricultural sector.
The Department of Homeland Security recently released a report addressing these issues - Threats to Precision Agriculture addresses the security threats related to the adoption and impact of new digital technologies in crop and livestock production. Here are three takeaways from this report.
Threats to Confidentiality
Data privacy is a top concern when implementing precision agriculture. Farmers are very protective of their information, such as yield data, land prices, and herd health. Loss or misuse of the data can have dramatic financial and emotional impacts on farmers. There is also a potential reputational loss for equipment and software manufacturers.
Threats to Integrity
Precision agriculture has aggressively moved into “smart farming” with the introduction of massive sensor nets being built in the crop and livestock sectors. Data collection and exploitation is a valuable tool assisting in real-time farming and livestock decisions. As precision agriculture increasingly adopts equipment automation, robotics, machine learning, and edge computing, threats to data integrity are manifesting in ways never contemplated in the agriculture sector.
Threats to Availability
Farming and livestock operations are heavily equipment reliant. Major farm equipment is a system of systems, relying on complex embedded tools, and a sophisticated suite of communication and guidance systems. Threats to equipment availability manifest from both cyber-related issues, and natural disasters. What became evident was the impact to equipment loss is very uneven and is heavily timing dependent.
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