Why Cybersecurity needs to be a priority for the Agriculture Industry ?
Agricultural cybersecurity is a rising concern because farming is becoming ever more reliant on computers and Internet access. During the last few years, the agrotechnology community, public sector and researchers have been alerted to the problem and a significant amount of research has focused on the issue.
Business Risk
Why Industry is a target for cybercrime
Criminals think that larger businesses have the resources to pay the demands without a second thought and smaller businesses often lack the necessary updates that are needed to fend off cybercriminals. For instance, a farm services company in Iowa, NEW Cooperative Inc, recently took its systems offline to contain a security threat. A notorious criminal group known for ransomware attacks took credit for it.
Cybersecurity in aerospace is a priority.
If delayed can become a liability.
Technical Threats
How Industry Is Targeted
The digital transformation of the agriculture sector is expanding it from the physical world into the cyber realm. While the adoption of internet of things (IOT) and smart technologies opens the door to innovation and new efficiencies, it also exposes the sector to new cyber threats.
E-Crime operations are perpetually looking for new victims, especially among those larger businesses perceived to have a high capacity to pay. There are multi types of threat like, Hacktivist Threat, Targeted Threat, E-Crime Threat
The challenges Industry is facing
Meet rising demand for more food of higher quality
Stay resilient against global economic factors
Adopt and learn new technologies
Cope with climate change, soil erosion and biodiversity loss
Satisfy consumers' changing tastes and expectations
Investment in farm productivity
Experts Tips
Top tips for securing Agriculture Industry
Using AI and Machine learning
Using AI and Machine learning-based surveillance systems to monitor every crop field’s real-time video feed identifies animal or human breaches, sending an alert immediately. AI and Machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones.
Yield Mapping
Yield mapping is an agricultural technique that relies on supervised machine learning algorithms to find patterns in large-scale data sets and understand the orthogonality of them in real-time, all of which is invaluable for crop planning.