AI Energy OT Threat Detection

The Problem

Detect OT cyber threats before outages occur

Organizations face these key challenges:

1

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

2

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

3

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

The Shift

Before AI~85% Manual

Human Does

  • Review every case manually
  • Handle requests one by one
  • Make decisions on each item
  • Document and track progress

Automation

  • Basic routing only
With AI~75% Automated

Human Does

  • Review edge cases
  • Final approvals
  • Strategic oversight

AI Handles

  • Automate routine processing
  • Classify and route instantly
  • Analyze at scale
  • Operate 24/7

Technologies

Technologies commonly used in AI Energy OT Threat Detection implementations:

Real-World Use Cases

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