An AI revolution in the energy business?
The widespread introduction and adoption of artificial intelligence (AI) in the electric power industry will revolutionize the energy and utilities business, from optimizing power production, trading, distribution, and consumption to enhancing safety and efficiency and reducing cost.[1][2]
While there is significant excitement surrounding AI, the primary impetus for AI adoption in the electric power sector stems from the escalating complexity of energy systems.[3] Traditional technologies, such as physical models for energy flow management, are becoming inadequate to address these complexities.[4]
AI has dozens, or maybe hundreds, of applications and use cases within the electric power value chain, including renewables (e.g. offshore and onshore wind, solar, hydrogen),[5] transmission and distribution grid operations, virtual power plant management, electric vehicle integration, and more. AI can be applied to optimize various processes.
Common AI techniques utilized in the electric power sector include deep learning, reinforcement learning, and hybrid models. Generative AI (GenAI) is a subset of these techniques, focused on a particular set of use cases, often using large language models (LLMs).
However, the adoption of AI in the electric power business comes with significant risks that must be managed.[6][7] Advanced security technologies and zero-trust strategies can mitigate these risks, ensuring the secure, reliable, and efficient use of AI.
Let’s dive into the data-driven challenges and potential pitfalls of AI in the electric power business. We’ll then uncover innovative technologies, including zero-trust frameworks, that can help navigate these complexities and unlock the full potential of AI.
AI challenges and risks for the electric power industry
Given the energy industry’s vital role in our society, its adoption of AI must navigate significant challenges and risks. A compromised AI system could result in widespread disruptions, particularly for critical infrastructure such as renewable power generation, power grids and virtual power plants.
Data and cyber security vulnerabilities. AI systems rely heavily on data collected from OT and IT systems (sensors, meters, grid equipment, third-party data sources, and more). This reliance creates multiple entry points for attacks: (a) data poisoning — attackers could introduce false data to manipulate AI algorithms, leading to incorrect predictions or decisions that disrupt energy operations; (b) ransomware attacks — hackers targeting AI-driven energy infrastructure could hold critical data hostage, threatening blackouts or financial loss; (c) supply chain risks — vulnerabilities in third-party AI or data tools used by energy companies could compromise overall system security.
Data privacy concerns. IoT-enabled devices in particular collect vast amounts of data, including detailed energy consumption patterns. This data is critical for AI algorithms to provide insights and optimization. However, it raises significant privacy concerns such as consumer profiling and unauthorized access. Complying with privacy regulations like GDPR is a fundamental challenge for energy companies using AI, as they must balance operational needs with consumer privacy.
Data bias and algorithmic errors. AI models require high-quality, representative datasets for training and decision-making. Incomplete, biased, or outdated data can result in flawed models with negative consequences.
Data ownership and sharing. AI often involves collaboration between multiple stakeholders, including utility companies, technology providers, and governments. This creates challenges around data ownership and interoperability. Differences in regulation, data standards and formats can hinder seamless data sharing and AI implementation.
Regulatory and compliance challenges. The industry is highly regulated, and the use of AI adds complexity to compliance. Traditional energy regulations may not account for the dynamic, data-driven nature of AI. Ensuring that AI-driven decisions comply with regulations also requires robust auditing processes and explainable AI models. Global companies must navigate varying data protection and AI governance rules across jurisdictions.
How to mitigate and manage risks in AI-powered energy
Addressing and managing AI risks in the energy industry is a complex task requiring a collaborative effort from various stakeholders (energy companies, tech providers, energy consumers, policymakers and regulators). Key strategies include:
Fortifying data and cybersecurity — implementing advanced security technologies and zero-trust principles to protect sensitive data and AI systems from data and cyber threats.
Safeguarding data privacy — employing anonymization and encryption techniques to safeguard data while adhering to privacy regulations.
Establishing data governance — developing clear policies for data ownership, usage, and sharing to foster trust, interoperability and accountability.
Enhancing data quality — continuously monitoring and improving data to minimize biases and errors in AI models.
Collaborating on regulation — working with policymakers to create AI-specific regulations that balance innovation with safety and compliance.
By adopting these measures, the energy industry can harness the power of AI while mitigating potential risks. How can the data-related strategies be implemented?
Can zero trust safeguard our AI-powered energy future?
AI methods introduce unique risks to the energy business, from data breaches and manipulation to algorithmic vulnerabilities. Zero-trust security models and architectures (ZTA) are a transformative approach to digital security, designed to address these complex risks.[8]
Unlike traditional perimeter-based security, which assumes that users and devices inside the network are trustworthy, zero trust operates on the principle of “never trust, always verify.” This proactive stance is especially valuable for managing AI-related risks in energy systems, where data sensitivity, operational reliability, and data threats converge.
Key principles of zero trust in AI-powered energy
Four principles guide zero-trust strategies for AI:
Identity verification everywhere. Enforce continuous authentication of users, devices, data, and services to ensure that only authorized entities gain access to sensitive resources.
Least-privilege access. Implement strict access controls to limit user and device permissions to only the minimum necessary to perform their assigned tasks.
Micro-segmentation. Divide the network into smaller, isolated segments to contain potential breaches and prevent attackers from moving laterally within the system.
Assume breach. Adopt a proactive security posture by assuming that breaches will occur and focusing on rapid detection, response, and recovery mechanisms.
Applying zero trust to AI security in energy
Zero-trust principles can be directly linked to the risks of AI projects in energy:
Securing AI training and deployment pipelines. The training and deployment of AI models rely on extensive datasets and complex infrastructures. To ensure data integrity, secure model access, tamper resistance and robust pipeline monitoring, zero-trust architectures are essential. They provide a robust framework for enforcing strict access controls and continuous verification.
Protecting AI-driven energy operations. AI-driven real-time energy management systems require very robust security measures. Key strategies include IoT device and data authentication to ensure only authorized IoT devices and data can interact with the AI, segmentation of critical systems, and continuous behavior analysis to detect and mitigate cyber threats.
Managing third-party risks. To address supply chain risks associated with third-party technologies, a zero-trust approach limits third-party access to necessary resources, uses encryption and verification mechanisms to protect sensitive data, and relies on regular audits to assess compliance with zero-trust security standards.
Which technologies enable zero trust in electric power AI systems?
Advanced technologies can empower the energy industry to implement zero-trust security for AI, safeguarding critical infrastructure and accelerating innovation.
Explicit Private Networking (XPN)
XPN is designed with a zero-trust approach at its core. It assumes no entity — internal or external — is inherently trusted and ensures that every interaction is explicitly verified. XPN provides a strong foundation by securing both endpoint devices and data, leveraging strong identity verification, strict granular policy-based access control, real-time monitoring, and encryption. Its design philosophy minimizes the attack surface and assumes the inevitability of breaches, providing a highly secure environment for sensitive resources.
How XPN supports and secures the AI journey in energy
When designing, exploring and training AI models, XPN ensures that data transmitted between devices and AI models remains untampered through encryption and strict access control mechanisms. By isolating AI data pipelines within private, encrypted networks, XPN prevents adversaries from accessing or manipulating sensitive data. Real-time monitoring and logging within XPN ensure that any unauthorized attempts to alter data streams are quickly detected and mitigated.
The same applies once AI is implemented in energy operations. By protecting the flow of real-time operational data, XPN ensures accurate and trustworthy outputs from AI systems. XPN also protects commands that are sent back to devices. Even under active attacks or network disruptions, XPN keeps secure pathways between AI systems and OT components operational.
XPN creates isolated environments for third-party interactions, ensuring that external vendors cannot access more resources than explicitly authorized. It enforces strict access policies, allowing third parties to access only the specific datasets or systems they require. Logs and audits within XPN provide full visibility into third-party activities, enabling quick detection of and response to suspicious behavior.
Additional technologies help secure the use of AI in energy, such as AI-enhanced anomaly detection (AI monitoring network traffic and user behavior and flagging suspicious activity in real time), advanced identity and access management, and more.
Zero-trust challenges for AI in energy
Implementing zero-trust strategies is not without challenges, particularly for legacy electrical infrastructure. It takes time and investment to adapt to a security model that prioritizes continuous verification and least-privilege access. As energy systems grow increasingly complex and interconnected, maintaining zero-trust principles demands robust tools (such as XPN) and processes that scale. A cultural shift is also necessary within organizations to embrace a security-first mindset — integrating zero-trust principles into every aspect of operations, from development to deployment.
Collaborative technical standards initiatives like the Trusted Energy Interoperability Alliance (TEIA) — founded by E.ON, JERA, Origin Energy, GS Energy and Intertrust — offer valuable guidance and support for implementing zero-trust security in the energy sector.
The way forward
While AI offers immense potential to revolutionize the energy industry, its benefits are accompanied by significant risks. To fully harness AI’s power, a proactive approach from the energy industry is crucial, combining technological innovation with regulatory and organizational measures. By building robust safeguards, the energy industry can create growth and a more efficient, sustainable, and secure energy future.
References
[1] Safari, A. et al., A Systematic Review of Artificial Intelligence for Energy Management. Appl. Sci. 2024, 14, 11112. https://doi.org/10.3390/app142311112
[2] McKinsey, Beyond the hype: New opportunities for gen AI in energy and materials, February 2024. https://www.mckinsey.com/industries/metals-and-mining/our-insights/beyond-the-hype-new-opportunities-for-gen-ai-in-energy-and-materials
[3] Vida Rozite et al., Why AI and energy are the new power couple, IEA, November 2023. https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple
[4] A.T.D. Perera et al., Applications of reinforcement learning in energy systems, Renewable and Sustainable Energy Reviews, Vol. 137, 2021. https://doi.org/10.1016/j.rser.2020.110618
[5] Song, D. et al., Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems. J. Mar. Sci. Eng. 2024, 12, 424. https://doi.org/10.3390/jmse12030424
[6] US Department of Energy, Potential Benefits and Risks of Artificial Intelligence for Critical Energy Infrastructure, April 2024. https://www.energy.gov/sites/default/files/2024-04/DOE%20CESER_EO14110-AI%20Report%20Summary_4-26-24.pdf
[7] D. Sandalow et al., Can AI Transform the Power Sector?, Columbia Center on Global Energy Policy, December 4, 2024. https://www.energypolicy.columbia.edu/can-ai-transform-the-power-sector/
[8] Julian Durand, Zero Trust Architecture: The Unapologetic Approach To Cybersecurity In A Digital Jungle, Forbes Technology Council, September 14, 2023. https://www.forbes.com/councils/forbestechcouncil/2023/09/14/zero-trust-architecture-the-unapologetic-approach-to-cybersecurity-in-a-digital-jungle/