If you work in critical infrastructure security, you already know the feeling: you bookmark one article about AI’s impact on the energy and security industries, and by the time you finish reading it, three more have dropped. This week alone, it was revealed that Constellation is doubling down on domestic nuclear energy, and Oracle and Bloom Energy said they are expanding their fuel cell agreement to 2.8 gigawatts. And earlier this month, Anthropic revealed its new model, Claude Mythos, exhibited striking capabilities to exploit even the smallest cybersecurity vulnerability that were “in a different league,” sending shockwaves and worry through the cybersecurity industry.
Summary
The AI energy buildout is moving faster than anyone can keep up with, including regulators. And underneath every gigawatt of new capacity is a growing attack surface that most operators aren't ready for. The opportunity in AI-powered energy is real, but so is the exposure. CEOs who treat security as the foundation of that opportunity, not a line item bolted on afterward, are the ones who will be best positioned to propel their companies into the next generation of power and cybersecurity. InfraShield's Elizabeth McAndrew-Benavides provides 5 key technical pillars that every critical energy CEO needs to know as they balance risk, cost, and compliance in this new age.
As private industry attempts to keep pace with the speed of AI innovation, federal regulators are also struggling to keep up. The Federal Energy Regulatory Commission (FERC) has set a June deadline to decide how it will use regulatory authority over the electricity grid as power demand surges. Weeks ago, the Department of Energy acknowledged in its FY2027 budget that grid cybersecurity is central to modernization. The DOE has also launched Genesis Mission, a submissions-based project for AI-backed innovations to the grid with first submissions due later this month.
The pace is dizzying and CEOs of major critical infrastructure facilities must balance risk, cost, and governance as they adapt security systems to the new technologies.
News of the promises and perils of AI are pushed out faster than the newest version of your favorite LLM, and faster than the regulators tasked with ensuring the industry is safely moving forward can process it. The opportunities embedded in these rapid shifts are massive for the operators, investors, and enterprises smart enough to move decisively. But the risks are also significant, especially in the critical infrastructure sector. Every gigawatt added to this buildout is also a gigawatt of new attack surface. A senior U.S. energy official told Semafor in March that as the grid expands to keep up with the AI race, "you have a growing surface of attack on which our adversaries can target us.”
None of this is hypothetical. Earlier this month, a joint advisory from the FBI, CISA, NSA, DOE, and U.S. Cyber Command confirmed that Iran-linked actors have actively disrupted U.S. energy, water, and government infrastructure by targeting programmable logic controllers, the same OT devices that underpin grid automation across the country.
As AI is increasingly embedded in critical infrastructure systems, these risks similarly increase.
At InfraShield, we are staying on top of all these advances for our clients from the lens of what they mean technically for their operations and security. We have always counseled our clients that cybersecurity is not about meeting minimum requirements, but understanding how systems actually behave and designing controls accordingly. That is especially true in the age of AI.
Advances in AI are vital, but in the absence of risk-informed regulations, leaders and investors must take responsibility for securing their AI deployments and ensure security protocols are prioritized alongside efficiency goals. We are here to help you do that. To fully understand how AI advances interplay in their sector, critical infrastructure CEOs must grapple with five foundational technical realities that materially influence AI risk, cost, and governance decisions. These are not abstract technical issues. They are decision variables that directly impact cost, resilience, and regulatory exposure.
1. Prompt Injection: a new attack pathway
Prompt injection is essentially social engineering directed at an AI system. Because models are designed to follow instructions, malicious inputs may attempt to override safeguards or extract sensitive information. When AI systems are connected to enterprise data sources or operational tools, this creates a new category of cybersecurity risk that traditional controls do not fully address.
2. Training Data vs Retrieval: two different risk surfaces
Base models generate responses from training patterns, while retrieval systems supply external information dynamically. These introduce different governance considerations. Training data raises questions about bias persistence and model limitations. Retrieval introduces considerations around data provenance, confidentiality, and integrity. Many AI systems rely on both, which complicates oversight approaches.
3. Tokens: the hidden cost driver
AI models process text as tokens, not pages or documents. Every interaction consumes tokens, which directly affects cost, latency, and performance. Organizations often underestimate how quickly token usage scales once AI is embedded into workflows. Automation multiplies usage, large documents increase cost, and inefficient prompts introduce both expense and variability. Token economics quietly shape architecture and security decisions.
4. Context Window: what the model can see at one time
AI models can only evaluate a limited amount of information at once. This bounded “working memory” is called the context window. Long procedures, regulatory documents, and technical materials often exceed this limit, meaning related information may be analyzed separately rather than together. How information is structured directly affects output quality and reliability.
5. Model Weights: how AI actually “learns”
AI systems do not store knowledge like a database. Instead, training adjusts mathematical weight relationships that influence how the model interprets patterns. The model’s behavior reflects statistical relationships learned during training rather than deterministic logic written by programmers. This distinction has important implications for validation, explainability, and regulatory oversight.
The AI energy buildout is not slowing down, and neither are the adversaries survelling it. Every new reactor deal, every gigawatt of data center capacity, every efficiency gain unlocked by an AI-powered grid is also a new attack vector, a new dependency, a new question about who controls what when something goes wrong.
At InfraShield, we don't think security should be an afterthought bolted onto innovation. We think it's the precondition for innovation that lasts, and none that must be driven by private industry and guided by regulators.
Media Contact
Rob Legare
rob.legare@BlueHighwayAdvisory.com
