Automated Network Anomaly Detection in Manufacturing

AI learns your facility's unique operational baseline - normal data patterns during production runs, expected SCADA communication cycles, and legitimate material planning queries - then identifies genuine threats by detecting deviations that correlate with actual production context rather than generic rules. Unlike standard SIEM tools, the system understands that high-volume data transfers during scheduled month-end SAP reconciliation are normal, while similar transfers at 2 a.m. on a Sunday are anomalous. It integrates work order schedules, shift timing, and asset registries from your MES and ERP, so every network event is evaluated against what should actually be happening on your plant floor at that moment.

Yes. All data remains on-premises or in your designated private cloud environment. Manufacturing-specific regulations like ITAR export controls and EPA emissions reporting are preserved because audit logs never leave your infrastructure; the AI system operates as an internal service, not a third-party SaaS. Your compliance team retains full chain-of-custody documentation for regulatory reviews.

Plan for a working system inside the first 100 days. Weeks 1-3 cover infrastructure setup and data pipeline configuration - network tap deployment, API integration with SAP, MES, and SCADA; weeks 4-8 focus on baseline learning and model training using 90+ days of your historical operational and network data; weeks 9-12 include pilot testing with your IT team and tuning detection rules. The manufacturing-specific variable is baseline depth and access: plants with accessible SCADA historians and clean SAP work order feeds move fastest, while plants with hard OT/IT air gaps spend part of the window on integration plumbing before training starts. A rollout like this is scoped to show measurable results - reduced false positives and first validated threat detections - within 60 days of go-live, with full ROI visibility by month four.

Three that a plant manager would recognize. Downtime avoidance: threats get contained before they stop a line, which is the whole game. Signal over noise: your analysts see a short list of pre-investigated notifications with production context instead of hundreds of raw alerts. And audit readiness: continuous monitoring evidence, investigation records, and containment documentation accumulate automatically, in the format ISO 9001 and ITAR reviewers actually ask for.

Only yours. The baseline that flags anomalies is built exclusively from your own SAP, MES, and SCADA history - no other client's packet flows, work orders, or asset registries ever inform what counts as normal on your plant floor, and nothing your facility generates trains a shared or cross-customer model. Updates to the baseline come from your own analysts approving or correcting flagged events, not from aggregated data across our client base. If your legal or security team wants that isolation guarantee in writing rather than taking our word for it, it goes in the contract, not just a compliance page.

If SCADA historians and MES logs sit behind a hard OT/IT air gap nobody's allowed to cross, the model has no production context to correlate against network traffic, and the audit will surface that before any integration work starts. Same if your plant has under 90 days of stable operating history because of a recent line reconfiguration or ERP migration - deploying on top of an atypical baseline just recreates the alert-fatigue problem. And if there's no analyst on staff to review the weekly high-confidence queue, automated containment on OT segments is not something we'll turn on unsupervised.

It ties every network event to what the plant is actually doing at that moment. A data surge during a scheduled batch run reads as production, not exfiltration, because the model sees work order schedules and changeover logs alongside packet flows. It knows which devices are supposed to talk to each other because it reads your asset registry. And as the operation changes - new lines, new shifts, new products - analyst feedback retrains it, so the baseline follows the plant instead of freezing at deployment day.