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Where AI Anomaly Detection Helps — And Where Rules Still Win
By Engineering — Algorithms · February 8, 2026 · 6 min read
We use both. The interesting question is which decisions belong to which approach. The split is not where most marketing decks would put it.
The "AI-powered" claim is doing a lot of work in our space. The honest answer is that we use machine-learned models for some decisions and explicit rules for others, and we are quite particular about which is which.
Where rules win
- Trip conditions for a red-state alert. These have to be inspectable, auditable, and class-society-presentable. A neural network is not.
- Network health (cable break detection, node liveness). Deterministic, not probabilistic.
- Calibration drift compensation. Linear, well-understood physics.
Where models help
- False-positive suppression. The space of nuisance signatures is too varied for explicit rules.
- Per-deck baselining. Different ships, different cargo mixes, different baselines.
- Anomaly clustering for post-voyage analysis.
The reverse split — rules for nuisance suppression, models for alarm trips — is a common mistake and produces systems that are both noisy and opaque.