Asked what the top priority for governing AI should be, as it increasingly coordinates energy across borders, energy leaders answer in the language of specification. Security and system reliability comes first, at 53%. Transparency and accountability of AI decisions follows at 44%, and common technical standards and interoperability at 40%. Protecting national energy sovereignty is named by 24%. Read as a design brief, it is extremely coherent: make it secure, make its decisions inspectable, and make its parts work with one another. These are requirements rather than candidates — a description of the properties leaders want the system to have, whoever builds it. They are also clear about where to start. Asked which areas of the energy sector would benefit most from sector-wide data standards for AI, 41% name grid management and optimization — more than twice the next answer, predictive maintenance at 18%.A year ago the same question produced the same first answer at 39%.Through a period in which almost everything about AI in energy moved, this one conviction has not. It is the closest thing the report contains to a settled engineering priority. As AI begins coordinating energy systems across assets, companies and borders, leaders are converging on a clear set of priorities: security, transparency and shared standards. The priority now is creating the conditions for AI to operate safely, reliably and at scale. The supporting picture points the same way. Leaders were asked what is most important to incorporating more AI into the energy sector, and the answers have tilted over 12 months toward the architecture around the technology rather than the instruments feeding it: improving cybersecurity frameworks is named by 31%, up from 27%, and improving the regulatory framework by 18%, up from 15%, while sensor deployment has fallen from 31% to 24%. The sector spent recent years working out how to measure things. It is now working out how to govern what the measurements set in motion. One item on that brief is missing, however. Forty-four percent of leaders want AI's decisions to be transparent and accountable. Far fewer — 18%, the second lowest- ranked of the six priorities — ask for clear lines of liability when a system fails.The difference between the two is important. Transparency tells you why an automated decision was made. Liability settles who is answerable for it. As AI takes on decisions that move energy between markets, the second question becomes harder to leave open. That gap aside, what stands out is that this year’s report lands in familiar territory. When leaders described what holds AI back, the obstacles were about permission and ownership more than technology. When they were asked what policy should do, they reached for system integration and cross-border coordination. Now, asked how AI should be governed, they specify standards, interoperability and shared conditions. Three different questions, one consistent answer: the work that matters most sits between organizations, not inside them. What leaders are describing is infrastructure as much as policy. Standards, interoperability and the ability to verify a decision must be built, run and powered. The objective should be dependable cooperation with clear control, not technological self-sufficiency. 8 COOPERATION IS KEY FOR AI GOVERNANCE 30