Across the energy industry, professionals see advanced data analytics, AI and machine learning, plus IoT, as being among the top technologies for investment, according to a DNV survey.
We cannot secure the energy transition without enabling the safe proliferation of emerging technologies. Companies must address vulnerabilities to ensure the secure and reliable operation of wind farms and the energy infrastructure they connect to.
Although digital technologies increase energy companies’ exposure to cyber risk, half (49%) of the energy professionals surveyed believe their organisations should accept this additional risk as a necessary tradeoff for innovation.
Securing data in AI systems
AI systems in the wind power industry rely heavily on training data to make accurate predictions and decisions, such as forecasting wind patterns or identifying equipment failures. The security of this data is paramount.
If training data is compromised, the entire AI model can be rendered ineffective or even dangerous. Adversarial attacks that manipulate input data could lead to false fault detection in turbines, for example, resulting in costly downtime or equipment damage.
To ensure the confidentiality of training data, companies should implement encryption and access controls. Data should only be accessible to authorised personnel, secure protocols should be put in place for data transmission and storage and the integrity data should be maintained to prevent tampering.
Ensuring the availability of training data is also critical. Redundant storage solutions and business continuity/disaster recovery plans can prevent data loss, which means there is a better chance that the AI systems remain operational.
Addressing vulnerabilities of IoT sensors
IoT sensors are the backbone of modern wind farms, collecting real-time data on wind speed, turbine performance and environmental conditions. However, these devices can be vulnerable to cyberattacks due to their limited computational power and lack of built-in security features.
A compromised IoT sensor can disrupt operations, falsify data, or even serve as an entry point for larger-scale attacks on the network.
The confidentiality of data collected by IoT sensors is at risk if bad actors intercept transmissions. Encrypting data in transit and using secure communication protocols can mitigate this risk. The integrity of sensor data is equally critical, as manipulated data can lead to incorrect decisions, such as overloading the grid or ignoring maintenance needs.
To help maintain data integrity, companies should implement digital signatures and ensure firmware updates. They should safeguard the availability of IoT sensors, as distributed denial of service (DDoS) attacks can overwhelm them. Network segmentation and intrusion detection systems can help prevent such attacks.
AI can itself play a role in securing digital systems. A quarter of the energy professionals surveyed say AI has already helped their organisation to strengthen its cybersecurity. Cybersecurity professionals understand that neglecting AI’s potential will put them at a disadvantage to the threat actors that are increasingly using these tools. Almost half (47%) fear they will fall behind the adversary unless they harness AI.
Enabling the energy transition
As the wind power industry embraces AI and IoT, it must also prioritise cybersecurity. This requires a multi-layered approach that combines technological solutions, employee training and regulatory compliance.
Collaboration between industry stakeholders, cybersecurity experts, and policymakers is crucial to develop frameworks and standards. By addressing these challenges, the wind industry can harness the full potential of AI and IoT while safeguarding its critical infrastructure.
In an era where renewable energy is more important than ever, securing the wind industry from attack is a global imperative.
Shaun Reardon is principal security consultant at DNV
“uåX˜äŠÊ˜·³Ç is now on Bluesky – follow us at for all the latest updates.
.png)
.png)
