Unlocking Industrial Edge Innovation – Beyond AI

AI leads the headlines. But industrial automation success requires attention to other technologies and business practices.

Industrial environments are notoriously diverse. They combine legacy industrial control systems (ICS), programmable logic controllers (PLCs), and supervisory control and data acquisition (SCADA) systems with modern IoT sensors, cloud-based services, and AI/ML workloads. This mix creates an intricate landscape that must operate cohesively under constraints that include limited bandwidth, intermittent connectivity, and resource scarcity.

AI might be part of the solution, but to tame the complexity and unlock innovation, industrial organizations need a cohesive strategy that includes the right security, the right technologies, and the right people with the right training. 

Strengthening Security in Distributed Edge Environments

As computation moves closer to where data is generated — on factory floors, oil rigs, or wind turbines — new security challenges emerge. Traditional IT security practices can fall short because they were designed for centralized, high-bandwidth environments. They struggle to account for the distributed, offline, and physically exposed nature of edge deployments, and they aren’t adaptable or granular enough to secure diverse edge nodes operating in harsh or remote conditions with limited oversight.

Many organizations are responding with a defense-in-depth approach:

• Zero-trust architectures: These include secure boot, trusted platform modules, and hardware roots of trust to ensure device integrity. They treat every connection as untrusted by default, ensuring least-privilege access across the environment.
• Immutable infrastructure and signed updates: Infrastructure cannot be changed once deployed, which reduces drift and tampering risks.
• End-to-end encryption: This is instituted to safeguard sensitive information — for example, TLS for data in transit and LUKS for data at rest.
• Identity and access management (IAM): These are among the practices that ensure that only explicitly permitted people and systems can perform an action on a particular resource. They bring robust authentication and access controls to remote nodes.  

Managing Risk and Compliance at Scale

In large-scale industrial ecosystems, it can be daunting to manage risk and compliance across thousands of edge devices. For example, each device may run a different software version, collect sensitive operational data, and be subject to varying regional regulations, making it difficult to maintain consistent security policies and audit trails. The scale and heterogeneity increase the risk of vulnerabilities slipping through the cracks, leading to potential downtime or noncompliance penalties. 

To address these weaknesses, customers tell us that they are adopting:

• Infrastructure-as-code and GitOps: Processes support automation of IT infrastructure for consistent, automated configuration and policy enforcement.
• Automated compliance monitoring and reporting: Activities detect and remediate drift.
• Open source frameworks and open standards: Examples including LFEdge and LFEnergy ensure interoperability and avoid vendor lock-in.

By embedding security and compliance directly into their core platforms, companies can reduce risk while accelerating innovation.

Putting AI and ML to Work — Sensibly

Thinking beyond AI does not suggest the subject is unimportant. Quite the contrary. In industrial organizations, AI and machine learning are enabling predictive maintenance, real-time quality inspection, and energy optimization – just as a starting point.

However, challenges remain around data availability, quality, and the ability to deploy complex models on resource-constrained edge devices to monetize the AI economy. These challenges limit AI applications’ scalability and reliability, especially in real-time edge environments where data is fragmented, unlabeled, or inconsistent. Additionally, deploying large models often requires trade-offs in performance, energy consumption, and latency — factors that directly impact the business case for AI monetization.

The keys to success lie in:

• Data access and governance: Structured approaches for creating and enforcing policies that control access to data, to integrate operational technology (OT) data sources such as SCADA and PLCs with IT platforms
• Efficient AI techniques: For using semi-supervised or unsupervised learning, synthetic data, and transfer learning — and knowing when to use them
• Secure AI models: Rapidly updated, audited, and validated
• Continuous improvement: For retraining models centrally, with the aim of boosting accuracy and adaptability over time
• Optimized edge AI models: Lightweight, hardware-accelerated models (such as those leveraging Nvidia GPU support) that are deployed for inference at the edge, while offloading heavy training workloads to centralized data centers

As companies transition from pilot projects to production AI, a focus on incremental wins and strong business cases becomes essential to demonstrating ROI and building organizational momentum.

Bridging the Skills Gap: The Human Element

Technology doesn’t deliver value. People do. The convergence of IT and OT demands a workforce that understands both domains, along with new technology approaches including AI, cloud, and automation.

To succeed, companies must:

• Invest in training and upskilling for both IT and OT teams.
• Embed change management to overcome resistance and drive adoption as new technologies reshape workflows, decision-making processes, and the balance between humans and machines.
• Reimagine human roles to shifting from “in the loop” to “on the loop,” where human oversight enhances machine autonomy.

Without a deliberate workforce strategy, even the most advanced technologies stall. 

Where eLxr Fits In

We are at an exciting inflection point in the industrial edge journey. There are opportunities to tame complexity with cloud-native integration and orchestration, to build security and compliance into the technology foundation (not as an afterthought), and to empower workforces to bridge the IT-OT divide.

Wind River is perfectly positioned to help you achieve those goals.

For example, by working with our partners, we extend what organizations can accomplish. eLxr Pro is a commercial-grade Debian Linux. It connects, orchestrates, and manages heterogeneous edge workloads while maintaining flexibility and scalability through partners such as Avassa. By combining Avassa’s edge orchestration technology with eLxr Pro, organizations can manage diverse edge environments, including seamless orchestration of containers and virtual machines.

Another example: eLxr Pro takes advantage of the Zededa and NVIDIA partnership ecosystem to solve real AI workload challenges, from seamless AI model development and optimization to secure deployment and zero-touch management, across diverse edge environments.

Wind River is no stranger to security concerns in industrial environments. With eLxr Pro, companies can deploy minimal, hardened operating system builds, automate over-the-air updates, and integrate compliance monitoring to meet global standards such as NIST, General Data Protection Regulation (GDPR), and the upcoming EU Cyber Resilience Act.

Furthermore, Wind River is perfectly positioned to help industrial organizations up-skill their employees’ tech knowledge, with eLxr Pro add-on professional services such as consultation hours, training, and security advisory services.

As the industrial automation market continues to grow, those who align technology, processes, and people are best positioned to thrive. With solutions such as eLxr Pro, industrial leaders are not just modernizing operations — they are shaping the future of resilient, intelligent, and human-centered industrial ecosystems.

By Jamie Helmer/Key Accounts for Industrial IoT, Wind River & Lexi Schroeder/Sr. Product Line Manager, Wind River