Automated OT Threat Detection and Containment

A mid-sized manufacturer runs a mix of legacy PLCs, modern PLCs, and office IT on a shared network. Security teams are overwhelmed by noisy alerts and slow root-cause analysis, which leads to long detection windows and recurring unplanned production downtime.

Combine streaming telemetry, unsupervised anomaly models and explainable graph analytics with SOAR for controlled response and human validation. The system continuously learns from labeled incidents, uses an LLM only for concise incident summaries (not raw decision-making), and enforces governance via playbooks, role-based approvals and digital-twin testing of containment actions.

• Collect and normalize telemetry from ICS/SCADA logs, packet capture (east-west traffic), endpoint agents and asset inventories into a time-series + graph store.
• Run unsupervised anomaly detection (autoencoders, isolation forests) and graph-based lateral-movement scoring to surface novel OT threats; enrich with supervised models for known signatures.
• Push prioritized incidents to a SOAR workflow that suggests containment actions (segmentation, port/block list, microseg policies) but requires operator approval for high-impact commands.
• Use an LLM to generate human-readable incident summaries and playbook recommendations; keep the LLM read-only over raw signals and log its outputs for audit.
• Governance: validate models in a sandbox/digital twin, maintain feature lineage, set approval thresholds, and perform periodic red-team and drift testing.

Teams typically see mean time to detect shrink from days to hours (illustrative range: 24-72 hours down to 1-8 hours) and SOC triage time fall by 30-60% through prioritized alerts and better summaries. A factory at this stage can expect unplanned production downtime tied to security incidents to decline by roughly 10-30% and overall incident response costs to drop by 20-40%, with false positives reduced via risk scoring and human-in-loop validation.