STRIDE - Secure and Resilient Infrastructure for Digital Enterprises

Aims and objectives

The project aimed to support industry in understanding and defining their long-term digital infrastructure needs. The project’s objectives therefore were to define a guiding framework and service based on real industry use cases to develop requirements for corresponding digital infrastructure.

Results and outcomes

The STRIDE project explored how Swedish industry can prepare for a new generation of digital and AI-driven solutions by developing a structured, human-centered way to define the digital infrastructure required for future applications. Today, manufacturers face growing challenges related to IT–OT integration, cybersecurity, regulatory demands, and the rapid expansion of AI technologies. STRIDE addressed these challenges through a unique combination of user-focused methods, hands-on testing, and analysis of emerging AI and cybersecurity risks.

A New Method for Digital Infrastructure Planning

A central result of STRIDE is the Guidance Service, a practical methodology that helps companies identify, refine, and validate the infrastructure they need for new digital solutions. The method is lightweight, repeatable, and incorporates real stakeholder needs rather than technology assumptions. It guides organisations from early exploration to clear, investment-ready requirements and can already be applied across different industrial contexts.

State-of-the-Art Insights: AI, Security and Compliance

The project also delivers an accessible overview of current risks to and from AI, relevant cybersecurity frameworks, and implications of the EU AI Act. These insights help companies navigate the fast-evolving landscape of AI governance, threat modelling, and compliance planning, and provide the foundation for RISE's AI risk classification approach for industrial applications.

Proof-of-Concept in Industrial Quality Inspection

To validate the method in practice, STRIDE carried out a Proof-of-Concept with real production data. Defect samples from laminate manufacturing were scanned using Mitsubishi Electric’s line-scan imaging technology and used to train an AI model. The PoC showed that both the imaging hardware and the AI model can detect defects with promising accuracy. This gives partners the confidence to move toward a future pilot and clarifies technical requirements for real-time industrial deployment.

Industry Impact and Future Opportunities

STRIDE has helped project partners understand what digital infrastructure they need, which technologies are ready for procurement, and where further development is required. The results also support Sweden’s ambitions for sustainable and innovative industry, aligning with Agenda 2030 goals on decent work, industry and infrastructure, and responsible production.

Future work may focus on scaling the methodology to new use cases, enabling reusability of AI models across industrial sites, and incorporating AI-assisted tools to analyse stakeholder insights more efficiently.

Read More

Full reports are available for organisations interested in applying the method or learning more about AI risk management and industrial quality inspection:

• STRIDE Final Report – Project methodology, PoC results, and recommendations
• AI Risk Classification for Visual Quality Inspection – compliance insights under the EU AI Act
• Security threats to and from AI models - A survey and state-of-the-art analysis

For collaboration opportunities or access to the Guidance Service, please contact the project leader.