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
The Shift
Human Does
- •Review OT logs, network alerts, and historian data for suspicious activity
- •Correlate signals across sites and assets to determine whether an incident is credible
- •Prioritize investigations based on asset criticality, safety exposure, and outage risk
- •Coordinate containment, site response, and recovery actions with operations personnel
Automation
- •Apply static rules and signature-based alerting for known threats
- •Aggregate available telemetry into basic alert queues and reports
- •Flag threshold breaches or predefined protocol violations
- •Provide limited historical search and trend views for manual analysis
Human Does
- •Approve response actions for high-risk OT incidents and operational exceptions
- •Decide whether flagged anomalies reflect malicious activity, process changes, or maintenance work
- •Escalate safety-critical events and coordinate containment with plant or grid operations
AI Handles
- •Continuously monitor OT network and process telemetry for abnormal control behavior
- •Baseline normal asset, unit, and site behavior and detect deviations in near real time
- •Correlate weak signals across protocols and data sources into prioritized incidents
- •Risk-score alerts using asset criticality, likely physical impact, and attack technique context
Operating Intelligence
How AI Energy OT Threat Detection runs once it is live
AI watches every signal continuously.
Humans investigate what it flags.
False positives train the next watch cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Observe
Step 2
Classify
Step 3
Route
Step 4
Exception Review
Step 5
Record
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.
The Loop
6 steps
Observe
Continuously take in operational signals and events.
Classify
Score, grade, or categorize what is coming in.
Route
Send routine items to the right path or queue.
Exception Review
Humans validate flagged edge cases and adjust standards.
Authority gates · 1
The system must not trigger containment or operational response actions for high-risk OT incidents without review and approval from OT security or plant operations personnel. [S1]
Why this step is human
Exception handling requires contextual reasoning and organizational judgment the model cannot reliably provide.
Record
Store outcomes and create the operating audit trail.
Feedback
Corrections and outcomes improve future performance.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Energy OT Threat Detection implementations:
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