Dual-use research is often described as a category problem, but in practice it is a decision problem. The question is not simply whether a technology has both civilian and defence relevance. The real question is how institutions should govern work that may create opportunity and risk simultaneously.
Artificial intelligence and advanced materials research illustrate the challenge clearly. Both domains can align well with major innovation and defence priorities, and both can raise concerns around export controls, partnership scrutiny, downstream use, and reputational interpretation.
Why binary classifications fail
Institutions sometimes look for a single threshold that will label work as either normal research or sensitive research. That is usually too blunt. Dual-use relevance sits on a spectrum that depends on:
- the capability being developed
- the maturity of the research
- the intended and plausible downstream applications
- the profile of collaborators, sponsors, or commercialization partners
- the policy environment surrounding that field
Binary treatment can miss important distinctions between ordinary collaboration, strategically relevant work, and research that merits stronger governance.
A more useful assessment model
Leadership teams benefit from a framework that evaluates at least three dimensions together:
Capability relevance
How closely does the underlying capability align with known defence, intelligence, or security-use cases?
Partnership exposure
Are there collaboration structures, funding arrangements, or external relationships that increase review requirements or institutional risk?
Governance readiness
Does the institution already have review pathways, escalation criteria, and decision ownership appropriate to the research?
When those dimensions are assessed together, institutions can distinguish between work that merely deserves awareness and work that requires tighter controls, stronger due diligence, or a different positioning strategy.
Strategy matters as much as risk
Dual-use analysis should not be framed only as a restriction mechanism. In many cases, the same assessment can identify where an institution is especially well aligned to emerging defence or resilience priorities. That insight can improve proposal strategy, partnership design, and internal investment decisions.
This is why dual-use governance should sit near research strategy, not only compliance.
Institutional next steps
Organizations working in AI, advanced materials, robotics, sensing, or related fields should consider creating:
- a repeatable dual-use screening framework
- escalation triggers for leadership review
- guidance for researchers and innovation teams on sensitive partnerships
- a portfolio-level map of areas with both opportunity and heightened scrutiny
Institutions that do this early are better positioned to navigate both opportunity capture and risk control without improvising under pressure.