Governing AI in the Energy Industry: Insights on Responsible AI
As artificial intelligence (AI) becomes more prevalent in the energy sector, the focus should shift to responsible governance. The energy industry, with its high stakes and critical infrastructure, faces unique challenges in adopting AI. This blog explores the practical realities, risks and governance strategies for Responsible AI in energy, featuring insights from EPAM AI and energy industry professionals.
AI governance emerged as a significant theme in EPAM’s AI research report “From Hype to Impact: How Enterprises Can Unlock Real Business Value with AI.” From the report and our vast industry experience, we learned that energy companies plan to roll out comprehensive AI governance models within the next 16 months to address concerns around ethics, security and transparency. Over 72% of the energy sector respondents said that data governance is important when investing in AI, and over 74% indicated that data quality plays a critical role. Of the 11 top challenges and concerns energy organizations have faced in adopting AI, five have to do with governance concepts.
Data security risks, including breaches and misuse in large language models (LLMs), are particularly pertinent in the energy sector, where there are a lot of proprietary operational data and trade secrets. Governance frameworks for responsible AI are important for alleviating these concerns and scaling AI solutions enterprise-wide.
The Stakes: Why Responsible AI Is Essential
The energy sector is not just another industry experimenting with AI. The consequences of failure are severe, and the risks are both physical and geographical in scope. As Sergey Sergeev puts it, “there is a high degree of physical risk with much of what we're doing in energy. So, any kind of system that operates within energy would not likely be fully automated using AI or a probabilistic system, just yet.”
In reality, this means that, for now, full automation is rare. Instead, AI is typically deployed in a “human-in-the-loop” model, where technology augments human decision-making but does not replace it. Sergey continues, “deterministic solutions are still the way to go, where you can clearly define the rule. If you cannot define the rule, you probably need a human-in-the-loop to approve the action.”
Key Governance Challenges
Several governance challenges exist in the energy sector:
- Human-in-the-loop and limited automation: Energy systems involve high physical risk, making full automation with AI undesirable. There is a strong preference for deterministic solutions where rules are clear and human approval is required for actions. Probabilistic AI systems are not yet trusted to operate independently in critical environments.
- Environmental impact: The energy sector already faces scrutiny for its environmental footprint. The use of large AI models, especially LLMs, adds further environmental burden (a “double whammy”), raising concerns about sustainability.
- Data sovereignty and privacy: Energy companies handle highly sensitive data, often tied to national interests and critical infrastructure. There is a need to protect data privacy and sovereignty, especially given legacy systems and the risk of exposure.
- Cybersecurity threats: The energy industry is a prime target for cyberattacks, including those from state actors. Incidents like the Colonial Pipeline hack in the U.S. highlight the vulnerability of critical infrastructure and the need for robust security measures.
- The LLM risk trifecta (often called the "lethal trifecta"): This security framework describes the three conditions under which an LLM-based system, especially AI agents that have access to tools, becomes highly vulnerable to exploitation. These conditions are:
- Access to private data: The AI can read sensitive information such as emails, internal documents or customer databases.
- Exposure to untrusted content: The AI processes data from the outside world that an attacker could manipulate (e.g., reading websites, summarizing emails, processing user logs). This creates the vector for prompt injection.
- Ability to communicate externally: The AI can send data out to the world (via emails, APIs or web browsing). This creates the vector for data exfiltration.
- Transparency and explainability: Most advanced AI models (e.g., LLMs) are “black boxes,” making it difficult for operators to understand and trust their recommendations. Transparency and explainability are essential, especially since engineers in energy demand evidence before taking action.
Responsible AI: Principles & Practices
The pillars of EPAM’s Responsible AI framework are:
- Human centricity
- Transparency
- Safety
- Governance
- Fairness and trustworthiness
When we talk about the energy industry, these pillars are interwoven into the principles and practices outlined below by our energy industry AI experts:
- Human oversight and limited automation
AI systems in energy are designed with human oversight as a core feature. Automation is limited, especially in areas with high physical risk. Human approval is required for actions, and deterministic solutions are preferred where rules can be clearly defined. If rules are unclear, a human must review and approve AI-driven actions. - Transparency and explainability
Transparency is essential for responsible AI. Operators and engineers need to understand why a system is making a recommendation. Transparency is achieved by exposing the logic behind AI outputs, such as revealing SQL queries or data sources. This helps build trust and enables validation before action is taken. - Mitigating over-reliance
A subtle but important risk is over-reliance on AI. There is a risk of operators becoming overly dependent on AI, leading to complacency. Responsible AI design should include measures to prevent blind acceptance of AI outputs, such as periodic testing with intentionally incorrect suggestions to ensure operator vigilance. Responsible AI governance must address this by designing systems that encourage vigilance. - Security and data control
Given the sensitivity of energy data, many organizations prefer on-premises solutions over cloud-based AI. Stephen Moody states, “we're seeing a load of this in Europe, even just with cloud platforms, people going back to on-prem. There's a lot of concern about the geopolitics of who owns these things and who has control of them.” - Regulatory alignment and risk management
Responsible AI is fundamentally a risk management process. AI systems must align with existing regulatory requirements for safety and transparency, especially in critical infrastructure. Responsible AI practices should be compatible with frameworks like the EU AI Act and other relevant regulations.
Practical Applications: AI in Non-Critical Areas
Currently, most AI applications in energy are found in non-critical areas, such as chatbots, advisory tools and predictive maintenance. Even in predictive maintenance, AI serves as an additional layer of insight, with human operators retaining ultimate authority. Responsible AI means not deploying AI in critical operations until risks are sufficiently understood and mitigated.
The Path Forward: Responsible AI as a Continuous Journey
Responsible AI in the energy industry is not a destination but a journey. It requires ongoing vigilance, collaboration and adaptation as technology and threats evolve. Fabio Mazzocchetti affirms, “responsible AI is about identifying risk and mitigating those risks. We haven’t seen a lot of responsible AI in the energy sector yet, and when LLMs get applied to critical areas, we will see more demand for it coming up.”
As AI becomes more deeply integrated into energy operations, the need for responsible practices will only grow. The industry has a unique opportunity to set the standard for responsible AI, balancing innovation with the imperative to protect people, the environment and the critical systems that power the world.
Responsible AI governance in energy is about more than technology — it’s about people, processes and a commitment to safety and ethics. By learning from real-world challenges and designing systems with these principles in mind, the energy sector can lead the way in responsible AI adoption.
