AI Energy OT Threat Detection

The Problem

“Detect OT cyber threats before outages occur”

Organizations face these key challenges:

Limited visibility into legacy OT assets and proprietary protocols; many devices cannot run agents and have sparse logging

High false-positive rates from IT-centric tools and rule-based OT IDS, creating alert fatigue and missed high-severity events

Slow, manual incident triage requiring specialized OT expertise and site coordination, increasing outage risk and safety exposure

Impact When Solved

Near-real-time detection of abnormal control actions (unauthorized writes, firmware/logic changes, atypical remote access) with risk scoring tied to asset criticalityAutomated correlation across network, endpoint, and process telemetry to reduce alert volume 30–60% and focus on high-consequence eventsImproved reliability and compliance posture by shortening MTTD/MTTR (70–90% and 40–60% respectively) and reducing likelihood of forced outages and reportable incidents

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

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0