Security by design in artificial intelligence-enabled energy management systems: a sociotechnical framework.

Putting artificial intelligence (AI) into modern energy‑management systems (EMS) brings both new efficiencies and new security risks. To handle those risks, we need a plan that mixes technical safeguards with human and organizational measures. This project framework looks at using a security‑by‑design approach for AI‑enabled EMS. It first examines cyber‑physical threats such as adversarial data injection and model poisoning, then builds a sociotechnical defense. On the technical side, machine‑learning models monitor grid parameters and detect anomalies in real time. On the social side, user‑access controls and operator verification protocols are added. Simulations model a decentralized microgrid where AI algorithms optimize power distribution while facing simulated cyber‑attack scenarios. The evaluation checks

that the system stays stable, that data remains intact, and that operations continue during adversarial interventions. This guidance helps engineering students see how cybersecurity, machine learning, and power‑systems engineering intersect, providing a clear path from concept to simulated validation of resilient energy‑management architectures.

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