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

Detects abnormal control commands, protocol misuse, and lateral movement in minutes instead of daysPrioritizes vulnerabilities and misconfigurations by operational criticality (e.g., feeder, substation, plant) to focus remediation where it matters mostImproves resilience and compliance by continuously validating segmentation, access patterns, and device posture across the OT/IoT fleet

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.

Time-SeriesEmerging Standard

8.5

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.