As of 2026, critical infrastructure faces risks where incident response speed and operational continuity are vital. The horizon of 2027 is relevant for considering Edge AI, as by the end of this period, 5G will become the dominant mobile access technology by subscription count, according to the Ericsson Mobility Report (November 2025). This will provide the necessary bandwidth and low latency for deploying complex AI solutions directly on-site. Integrating Edge AI with the Internet of Things (IoT) at the network edge not only enhances efficiency but also strengthens cybersecurity by minimizing risks associated with transmitting sensitive data to centralized cloud storage.
Security and efficiency challenges for critical infrastructure by 2027: the role of Edge AI
Critical infrastructure, from power grids to transportation systems, is a target for cyberattacks, and any disruption can have significant consequences. In 2026, we observe an increase in attack complexity, requiring security systems to have autonomous detection and response capabilities. Traditional approaches relying on centralized data processing often cannot provide the speed needed to protect operational technologies (OT) in real-time. Edge AI, by placing computation and data analysis closer to the sources (IoT devices), enables instantaneous anomaly detection, predictive equipment failure analysis, and workflow optimization without the latency caused by data transmission over the network.
This is particularly relevant for systems where every millisecond counts, such as in industrial process control or equipment health monitoring. AI models operating at the edge can analyze data streams from sensors, cameras, and other IoT devices, detecting security threats like unauthorized access or unusual device behavior, as well as operational issues like deviations from normal machine operation. This approach enhances the resilience and reliability of critical infrastructure.
5G and Edge AI: synergy for real-time data processing
The deployment of 5G networks is a driver for fully leveraging the potential of Edge AI in critical infrastructure. 5G’s high bandwidth, reaching tens of gigabits per second, and ultra-low latency (less than 1 millisecond) eliminate bottlenecks that previously hindered the transmission of large volumes of data from IoT devices to Edge servers. This allows Edge AI systems to receive, process, and analyze data almost instantly, which is crucial for real-time applications.
For example, in traffic management or energy system control, where high-resolution video streams and telemetry data from thousands of sensors need to be processed, 5G ensures a seamless flow of information. This enables Edge AI models to accurately and quickly detect incidents, optimize traffic flow, or energy distribution, preventing overloads and failures. Companies specializing in integrating such solutions, like Softengi, develop AI communication and anti-fraud models that can be deployed at the edge, leveraging the advantages of 5G for security.
Common mistake: evaluating AI solutions solely on technical accuracy
A common mistake in implementing AI solutions in critical infrastructure is focusing solely on the technical accuracy of the model (e.g., percentage of correct predictions). While accuracy is important, it does not always reflect real business value. For instance, a model that predicts equipment failure with 99% accuracy might be useless if its implementation does not lead to reduced downtime, cost savings in maintenance, or improved safety. It is essential to define clear business success metrics early in the project planning phase.
Instead of abstract accuracy, AI solutions should be evaluated based on metrics such as a reduction in unplanned stops by X%, a decrease in energy consumption by Y%, an increase in throughput by Z%, or a reduction in security incident response time by W%. This approach requires close collaboration between IT departments, operational units, and management to define real goals and expected outcomes. Without this, AI projects risk remaining expensive pilot projects with no tangible returns.
Data protection in critical infrastructure: Geopatriation and Confidential Computing
Data security is fundamental for critical infrastructure, especially when using Edge AI. Two technologies gaining significance in 2026-2027 are geopatriation and confidential computing. Geopatriation ensures that data is processed and stored exclusively within a specific geographic region or country, which is crucial for regulatory compliance and data sovereignty in sensitive sectors. This helps avoid jurisdictional risks and ensures compliance with local data protection laws.
Confidential computing, in turn, protects data while it is in use, i.e., during processing in processor memory. This is achieved by creating hardware enclaves (trusted execution environments) that isolate computations from the rest of the system, including the operating system and hypervisor. Even if an attacker gains access to the server, they cannot intercept the data or the AI model code running in the enclave. According to Gartner’s forecast, by 2029, over 75% of operations in untrusted infrastructure will be protected by confidential computing. This technology is particularly important for Edge AI in critical infrastructure, where edge devices can be physically vulnerable. Companies like IQusion, specializing in comprehensive cybersecurity solutions, are already integrating these approaches to protect clients’ sensitive data.
Furthermore, the development of domain-specific AI models is important for security. Gartner predicts that by 2028, more than half of GenAI models used by enterprises will be domain-specific. These models, trained on highly specialized data, are not only more accurate but also less susceptible to generalized attacks than universal models. Also by 2028, over 50% of enterprises will use AI security platforms that provide centralized management and protection of AI systems against specific threats.
Practical case: optimizing production processes with Edge AI
Consider the example of a large manufacturing enterprise operating a complex network of equipment that requires constant monitoring. Traditionally, data from SCADA systems and other sensors were collected and sent to a centralized cloud for analysis, leading to delays in fault detection. The implementation of Edge AI у on the AZIOT Platform (IoT platform for physical environment management from AZIOT) allowed computing power to be placed directly on the production lines.
IoT devices collect data on vibrations, temperature, pressure, and other machine operating parameters. This data is processed by Edge AI models in real-time, detecting the slightest deviations from the norm that indicate potential failures. For example, a model can predict a bearing failure several days before it actually occurs by analyzing microscopic changes in the vibration spectrum. This allows for predictive maintenance, preventing production downtime and significantly saving costs.
Additionally, Edge AI optimizes energy consumption by regulating equipment operation based on current load. Integration of OT/IT systems through the AZIOT Platform ensures seamless data exchange between production processes and corporate systems, such as ERP, allowing for automatic parts order generation. Data protection is ensured by processing sensitive information locally and using confidential computing for critical computations, minimizing the risks of cyberattacks and leaks.
Data readiness for AI: the foundation for secure and efficient solutions
Successful implementation of Edge AI in critical infrastructure depends on the quality and readiness of data. Even the most advanced AI models will be ineffective if trained on incomplete, inaccurate, or inconsistent data. This is particularly true for OT systems, where data is often stored in disparate formats without unified standards.
Before deploying Edge AI, thorough data preparation work is necessary, including:
- Data Governance: Establishing clear rules and processes for data collection, storage, processing, and usage, including access policies and quality standards.
- Data Quality Assessment: Evaluating the quality of existing data, identifying and correcting errors, duplicates, and missing values.
- Master Data Management: Creating and maintaining master data for key entities (equipment types, locations, incidents) to ensure data consistency.
- Data Integration: Building robust data integration mechanisms from various sources (SCADA, MES, ERP) to ensure a complete dataset for AI models.
Without proper data preparation, implementing AI solutions can lead to incorrect predictions and the creation of new vulnerabilities. Companies like Softline, specializing in system integration and data management, play a key role in creating a reliable foundation for Edge AI.
Critical infrastructure readiness checklist for Edge AI implementation
- Presence of an OT and IT system integration strategy.
- Defined business success metrics for AI solutions (ROI, error reduction, time savings).
- Implemented data quality assessment processes and master data management.
- Planned use of domain-specific AI models.
- Assessed the need for confidential computing for sensitive data.
- Developed a cybersecurity threat protection plan for edge devices.
- Availability of qualified personnel for managing and supporting Edge AI systems.