AI Energy IoT Security
The Problem
“Preventing cyber intrusions across energy IoT fleets”
Organizations face these key challenges:
Limited real-time visibility into heterogeneous OT/IoT assets, firmware, and configurations across substations, plants, and field networks
High alert volume and low signal-to-noise from signature/rule-based tools, leading to missed threats and analyst burnout
Operational constraints (uptime, safety, legacy protocols) restrict patching and active scanning, leaving vulnerabilities unaddressed for months or years
Impact When Solved
Real-World Use Cases
AI in Energy Industry: Smart Grid Optimization and Energy Management
This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.
Artificial Intelligence for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.