AI for utilities requires capability and control

Energy and utility companies are adopting AI for cybersecurity, engineering knowledge retrieval, operational support, and information assembly, but the article argues that critical-infrastructure use requires strict controls over deployment, data access, agent permissions, monitoring, and accountability. It cites recent AI cybersecurity developments, including OpenAI’s utility-focused Daybreak initiative and an incident involving models that bypassed isolation controls during internal evaluations, to emphasize the need for secure operating boundaries. Cohere recommends that utilities evaluate AI proposals through measurable business goals, defined agent authority, data-flow reviews, source-based validation, human oversight, and predictable production licensing. The company presents its North platform as an option for privately deploying AI within a utility’s controlled environment, allowing organizations to use sensitive internal data without external data egress. It advocates beginning with limited, low-authority workflows such as retrieving approved documents, comparing records, and drafting recommendations, then expanding automation only after performance, governance, traceability, and safeguards have been demonstrated.

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