Automated Network Anomaly Detection in Construction

AI learns the specific baseline behavior of your Construction systems - Procore uploads, Sage 300 transactions, Trimble GPS data, Primavera P6 schedule access - and flags deviations that indicate breach or unauthorized activity, eliminating the false-positive noise that blinds generic SIEM tools. The model adapts to your firm's operational rhythm: multi-site traffic patterns, subcontractor VPN usage, peak project activity windows, and normal seasonal staffing changes. It catches coordinated attacks - like a compromised account pulling RFI data from Procore while accessing schedules in Primavera - that point tools miss because they don't understand how your Construction systems interact.

Yes. All data remains on your infrastructure or within your cloud environment. We address Construction-specific regulatory requirements: OSHA 29 CFR Part 1904 digital record integrity, AIA billing system audit trails, and subcontractor data segregation. Encryption in transit and at rest, role-based access controls for your IT team, and access logs your auditors can verify keep your operational data under your control.

Plan for a working system inside the first 100 days. Weeks 1-2: infrastructure assessment and system integration planning with your Procore, Sage 300, and Trimble administrators. Weeks 3-6: data ingestion setup, baseline model training on 30-60 days of historical traffic. Weeks 7-10: pilot phase with your IT team reviewing flagged anomalies and refining alert thresholds. Weeks 11-14: full production rollout with automated response playbooks. The construction-specific variable is timing: a deployment that starts during a steady-state stretch trains faster and cleaner than one launched mid-migration or during a major project closeout, and if your logs are fragmented across Procore, Sage 300, and Viewpoint, expect more of the window to go to normalization before the model sees clean inputs. A rollout like this is scoped to show measurable results - reduced false positives, detected anomalies - within 60 days of go-live as the baseline model matures.

If your firm doesn't run at least one of the platforms this model trains on - Procore, Sage 300, Viewpoint Vista, Trimble - there's no traffic pattern to learn from yet, and the audit will say so rather than force a build. Same if no one on your team can own the weekly review of flagged anomalies; automated detection without a reviewer just moves the noise, it doesn't remove it. And if your logs go back only a week or two, the audit will point you toward fixing log retention first, not straight to deployment.

Two ways that matter. First, it learns your rhythm instead of enforcing an office-network template: 6 AM superintendent logins, Trimble GPS bursts from active sites, and month-end Sage 300 billing spikes all read as normal, because for your firm they are. Second, it keeps re-learning as operations shift - a new project ramping up, subcontractors onboarding, seasonal crews arriving - so the definition of anomaly tracks the business instead of drifting away from it and drowning your team in stale alerts.