Safeguarding AI: How Secure Data Transforms Energy Infrastructure

5 reasons why energy utilities (should) use AI

At the moment utility companies are facing a compelling case for modernizing energy infrastructure:

1. Enhanced Reliability and Resilience: Modernizing energy infrastructure equips utility companies with advanced monitoring and control systems that can quickly identify and respond to outages or disruptions. This improved reliability and resilience reduce downtime, ensuring a more consistent and dependable power supply for consumers.

2. Increased Energy Efficiency: Upgrading to smart grids and implementing energy-efficient technologies allows utility companies to optimize their operations, reducing energy losses during transmission and distribution.

3. Integration of Renewable Energy Sources: The transition to renewable energy sources like solar and wind power requires modernized infrastructure to manage the intermittent nature of these resources. Upgraded grids can efficiently incorporate and distribute renewable energy, contributing to a more sustainable energy mix.

4. Data-Driven Decision-Making: Advanced data analytics and real-time monitoring enable utility companies to make informed decisions about grid operations, load forecasting, and resource allocation. This data-driven approach helps optimize energy distribution and improve overall grid performance.

5. Cybersecurity and Resilience: With the increasing threat of cyberattacks on critical infrastructure, modernized energy systems require the implementation of robust cybersecurity measures to protect against potential breaches. Strengthened defenses enhance grid security and ensure uninterrupted service even in the face of cyber threats.

The energy consuming and producing behavior of our society is changing rapidly. Private households already set up solar panels on the roof, install heat pumps, battery home storage systems and EV charging stations – all either energy producing or consuming assets. Therefore, our grid needs to be adapted and expanded to withstand the increased loads. In a recent article of the Handelsblatt, this change has been confirmed by major energy providers like EON in Germany. Furthermore, it's stated that new technologies like AI play an essential part in ensuring grid stability. (Handelsblatt article)

It’s needed to strengthen the grid physically and digitally. The advantages of embracing cutting-edge technology for remotely monitoring primary and secondary substations are profound. By implementing such systems, utility companies can significantly enhance their operational efficiency and overall reliability. These remote monitoring solutions allow for real-time data collection, enabling swift detection of issues and proactive maintenance, which, in turn, minimizes downtime and reduces costly repairs.

Furthermore, when these remote monitoring systems are integrated with advanced AI/ML algorithms, the potential for optimizing energy distribution becomes even more compelling. These AI and ML tools can analyze historical and real-time data to identify patterns, anomalies, and trends, ultimately leading to more accurate load forecasting for electric grids. As a result, utility providers can make informed decisions to ensure a stable and resilient energy supply, minimizing the risk of blackouts, improving resource allocation, and promoting energy efficiency. As we delve deeper into this data-driven realm, we also unlock the potential for optimizing market and grid balancing strategies. In an era where sustainable and efficient energy distribution is paramount, the ability to predict and forecast load on electric grids is nothing short of a necessary game-changer.

Data Security is a prerequisite for successful energy transition

At the heart of the above outlined use cases is data. Whether it’s used for monitoring substations, training AI/ML models or optimizing grid balancing strategies. Therefore, it’s utterly important to properly secure that data, especially its integrity. Using tampered data for decision making in the critical infrastructure has fatal consequences that affect our whole society. One incident in the US already demonstrates how relevant this threat is now. The DARPA successfully executed a data tampering attack on the US power grid. They hijacked a smart sensor in a powerplant, tampered the sensor values and let the headquarter take wrong decisions, which lead to a blackout on Plum Island, NY. (Bloomberg report)

If we think the above outlined use cases end to end, in a next step the tampered values would be used as training data for the AI algorithm that should predict the energy loads. It’s obvious that an AI model which gets trained with malicious data is not going to make accurate predictions. However, the leverage poisoned data, in a training data pool, has on the final decision accuracy is shocking. Different studies proof that below 2% of poisoned data in a training data pool can increase the error rate of the final model by 80-90%. That’s the reason why ensuring data integrity from source over the entire lifecycle is paramount and regulatory authorities have the same view.

Different regions, different regulations, similar objectives

The US based National Institute of Standards & Technology (NIST) and its standards and frameworks have a very good reputation globally. Within a special publication focusing on data integrity NIST advises organizations on how to implement data integrity monitoring & protection across the whole infrastructure. In a nutshell it is recommended that integrity monitoring interacts with all enterprise components (e.g. applications, storage, etc.), client machines (e.g. devices, firewalls etc.), logging, event detection, forensics & analytics as well as mitigation & containment.

Furthermore, in the EU the new NIS2 directive is currently in the process of being implemented. The cyber security regulation addresses European critical infrastructure and enforces organizations to implement different cyber security controls and report incidents to dedicated authorities. Within NIS2 a cyber incident is defined as an “.. event compromising the availability, authenticity, integrity or confidentiality of stored, transmitted or processed data ..”. On the one side organizations need to implement controls that prevent incidents, and on the other side once an incident occurs they need to report it to the authorities. Hence, a wholistic data integrity monitoring will be needed for organizations operating in critical infrastructure.

In the middle east, especially Saudi Arabia, the National Cybersecurity Authority (NCA) is the government entity in charge of cybersecurity. Within different regulations the NCA aims to protect the Kingdom of Saudi Arabia. One of them is the Critical Systems Cybersecurity Controls (CSCC) that is an extension to the Essential Cybersecurity Controls (ECC) and addresses the cybersecurity needs for national critical infrastructure. One control requires organizations to: “Protecting systems’ logs and critical files from unauthorized access, tampering, illegitimate modification and/or deletion.”

How Tributech can help

Tributech enables energy companies to modernize and prepare the existing energy infrastructure for the upcoming energy transition and mitigate critical data security risks. The technology allows energy operators to audit the integrity and authenticity of data before making any decisions affecting the electrical grid. Furthermore, when training AI/ML models, organizations can verify data integrity and authenticity to get the most accurate results and mitigate the risk of training data poisoning. In this blogpost you can find more information how Tributech safeguards AI: Security Measures For AI | Tributech

Would you like to discuss your organization's data security strategy? Contact us and ensure the integrity of your data right from the start.