How fast must the model infer to be useful in OT?

For most loop-level anomalies, inference under 500ms per window is sufficient. For safety-critical systems, aim for <100ms and prioritize deterministic runtimes.

Do I need labeled fault data to start?

No — anomaly detection can start as unsupervised (train on normal operation). Labeled faults are extremely valuable later for supervised fine-tuning.

How do we avoid model drift?

Establish scheduled retraining, incorporate maintenance tags, and use rolling-window validation. Monitor reconstruction error distributions and set adaptive thresholds.

Can this run on existing PLC hardware?

Rarely. Most PLCs cannot host ML inference. Use an edge gateway or industrial PC colocated on the control network.

What about security and compliance?

Isolate inference gateways in a DMZ, use mTLS for telemetry, audit model updates, and keep a secure artifact store for model binaries.

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AI agents

Q: Do I need labeled fault data to start?

No — anomaly detection can start as unsupervised (train on normal operation). Labeled faults are extremely valuable later for supervised fine-tuning.

Q: How do we avoid model drift?

Establish scheduled retraining, incorporate maintenance tags, and use rolling-window validation. Monitor reconstruction error distributions and set adaptive thresholds.

Q: Can this run on existing PLC hardware?

Rarely. Most PLCs cannot host ML inference. Use an edge gateway or industrial PC colocated on the control network.

Q: What about security and compliance?

Isolate inference gateways in a DMZ, use mTLS for telemetry, audit model updates, and keep a secure artifact store for model binaries.

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AI OT anomaly detection: 7 Proven Ways to Cut Downtime

AI OT anomaly detection: 7 Proven Ways to Cut Downtime

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