New horizons in critical infrastructure security
As of 2026, critical infrastructure faces risks where incident response speed is vital. By the end of 2027, 5G will become the dominant mobile access technology, providing the necessary bandwidth and low latency for Edge AI deployment. Integrating artificial intelligence with IoT at the network edge improves efficiency and strengthens cybersecurity, minimizing the risks of transmitting sensitive data to cloud storage.
Traditional centralized approaches fail to protect operational technology (OT) in real time. Edge AI analyzes data streams directly on-site, enabling instant anomaly detection, predicting equipment failures, and optimizing processes without network delays.
The synergy of 5G and data protection
The ultra-low latency of 5G (less than 1 millisecond) eliminates bottlenecks when transmitting large volumes of data from IoT devices to edge servers. This allows Edge AI models to respond instantly to incidents in power grids or transportation. For instance, Softengi develops AI communications and anti-fraud models for edge deployment.
To protect information in 2026-2027, two concepts are becoming critically important:
- Geopatriation: processing and storing data strictly within a specific country to comply with regulatory requirements.
- Confidential computing: protecting data during processing in isolated hardware enclaves. According to Gartner, by 2029, over 75% of operations in untrusted infrastructure will be secured by this technology.
Furthermore, Gartner predicts that by 2028, more than 50% of enterprises will use AI security platforms, and more than half of corporate GenAI models will be domain-specific.
What it means for companies
The integration of Edge AI and 5G means critical infrastructure will transition from reactive defense to real-time, proactive threat mitigation. For businesses, this reduces costly operational downtime and ensures compliance with strict data residency laws through geopatriation, though it requires immediate investment in upgrading legacy OT systems.
Next steps
To implement these technologies successfully, enterprises must focus on data readiness. A successful example of implementation is the use of Edge AI based on the AZIOT Platform in large-scale manufacturing. Instead of sending data to the cloud, computations take place directly on the production lines. The system analyzes vibration and temperature, predicting equipment failures several days before they occur, which enables predictive maintenance.
System integrators, including Softline, emphasize the importance of preliminary preparation, which includes implementing Data Governance, assessing data quality, creating unified reference books, and integrating SCADA, MES, and ERP systems.
Enterprise readiness checklist for Edge AI
- Availability of an OT and IT system integration strategy.
- Defined business success metrics (ROI, downtime reduction).
- Implemented data quality management processes.
- Use of domain-specific AI models and confidential computing technologies.
- A cyber defense plan for edge devices and the availability of qualified personnel.
Prepared by a Software Ukraine member. Original publication.