Perspective_

Space is the ultimate high-risk environment. Whether you're designing architectures in low Earth orbit (LEO), planning logistics in cislunar space, or enabling sustained operations on the lunar surface, there is no margin for error, yet you’re operating in scenarios for which you often must overcome incomplete data and dynamic conditions. That's true in peacetime and, during a conflict, the stakes compound. Decisions get more complex, more consequential, and more compressed all at once.

Digital twins are how we continue to ensure investments in space succeed. By integrating multi-domain data and modeling the full mission lifecycle, they allow teams to test assumptions before capital is deployed; identify failure modes early; evaluate tradeoffs across performance, cost, and risk; and adapt in real time as conditions evolve.  

Anticipating adversaries

When addressing space warfare, you may be designing against a threat system you've never observed in its operational state and whose performance characteristics are genuinely unknown. Adversaries have developed sophisticated counterspace capabilities, from kinetic anti-satellite weapons to electronic jamming to cyberattacks on ground infrastructure. The 2022 ViaSat attack on the first day of Russia's invasion of Ukraine wasn't an anomaly, it was a preview. Orbital assets, military and commercial alike, are now legitimate targets.

This creates a planning problem. The U.S. Space Force is building and operating systems in a domain where the missions themselves are often novel, and the threat environment is partially obscured. Under those conditions, traditional system modeling breaks down; to understand how your system performs, you have to model the threat, too.  

Building twins on both sides of the problem

Digital twins of both Space Force systems and adversary threat systems enable simulation environments where both can interact, assumptions can be stress tested, and vulnerabilities identified before they become operational realities. Doing so often requires working across classification boundaries, which offers challenges ranging from stovepiped and often obsolete data to fidelity mismatches between models built at different design stages, to the friction that arises when crossing institutional security domains. This means that twins have to be able to work in a secure environment, and they need to pursue the right level of detail. A high-fidelity representation in one domain paired with a rough approximation in another can produce confident answers to the wrong questions. Engaging digital twins early — when the uncertainty is greatest and when decisions have the largest downstream impact — enables simulation environments to evolve with the mission: from concept through development, acquisition, test, and live operations. As real-world data flows in — ranging from sensor data to shifting telemetries — the models sharpen, and risk and uncertainty diminish, so that if and when threats emerge, operators are informed and ready.

Beyond the government mission  

The implications extend well beyond defense programs. For early-stage companies, digital twins can sharpen value propositions and de-risk investor conversations by grounding vision in operational reality. For more mature organizations, they accelerate iteration cycles and improve mission performance.  

Both the commercial and defense space sectors are making architecture decisions in the face of significant uncertainty. As we look at extending operations beyond LEO to cislunar space, the logistical and informational complexity increases by an order of magnitude. Operating between Earth and the Moon involves different physics, longer communication latencies, and a near-complete absence of the navigational and positional infrastructure we take for granted in LEO.  

A critical use of digital twins for both sectors, then, is to address space logistics as we move into increasingly novel missions. It's easy to optimize a single segment of a mission. It's much harder to track how decisions cascade across the full chain: launch, on-orbit operations, refueling, servicing, sustainment, and reconstitution under stress. Digital twins that model the full lifecycle, not just individual elements, are the only way to get ahead of that complexity.

Accelerating with AI

The new space race is accelerating, but so is the evolution of digital twins. With AI as infrastructure rather than a feature, twins can ingest new data, update models, run scenario analyses, and surface insights at machine speed, transforming what's possible in both mission planning and real-time operations. It can speed the production of models and enhance the ability to rapidly integrate across other digital twin environments.

As space operations push from LEO into cislunar space and toward the moon itself, the complexity of the environment, the logistics chains, and the threat picture will only increase. The organizations — government and commercial — that meet these challenges will be those that can understand and act on that complexity faster than their competitors. Digital twins, continuously updated and AI-augmented, are becoming a core part of that advantage, and supporting their ongoing evolution is how we keep pace with the upcoming challenges.