In the security-conscious sectors of defense, intelligence, and critical infrastructure, air-gapped systems remain a cornerstone of data protection. Their design, physically separated from external networks, offers a strong assurance of confidentiality. Yet as the demand for data-driven decision-making and AI integration grows, the question becomes: how can institutions collaborate effectively when one or more participants are operating inside an air-gapped environment?
This challenge is more than technical; it is strategic. Many of the most valuable insights come from partnerships that cross organizational, jurisdictional, and classification boundaries. But when data cannot be moved, shared, or exposed, traditional collaboration models fall short.
Fortunately, innovation is closing this gap.
Collaboration in air-gapped contexts is possible when the model shifts from data centralization to distributed, privacy-preserving computation. In this approach, data remains in place, inside its secure enclave, while approved models are deployed to run on it locally. Only the encrypted results or learned model parameters are shared outward, with no raw data ever leaving the system.
This design not only preserves the protective value of the air-gap; it also enables collaborative use cases once considered out of reach. For example:
These workflows are made possible through privacy-enhancing technologies (PETs) such as homomorphic encryption, federated learning, and secure enclaves. While each comes with specific technical constraints, their orchestration allows for flexible architectures that respect both security and mission needs.
These workflows are made possible through privacy-enhancing technologies (PETs) such as homomorphic encryption, federated learning, and secure enclaves. While each comes with specific technical constraints, their orchestration allows for flexible architectures that respect both security and mission needs. The Duality Platform was built specifically to operate in these constrained environments, including fully air-gapped networks. Its architecture provides a controlled and auditable mechanism for moving encrypted artifacts, such as FHE-encrypted data or model parameters, between domains, while allowing each domain owner to validate every input and output before anything crosses a boundary. This ensures that collaboration can happen without weakening the isolation guarantees that those environments rely on.
Importantly, the success of these collaborations depends not only on cryptographic strength but on trust frameworks. Governance, auditability, and policy alignment remain essential, especially in cross-domain environments. Collaboration succeeds when it is secure by design and governed by mutual assurance.
Air-gapped systems will remain a critical safeguard for some of the world’s most sensitive data. But that doesn’t mean collaboration has to stop. By designing workflows that move models instead of data, and building trust frameworks around that model, institutions can work together even when their infrastructure must remain isolated.
Isolation, then, isn’t a barrier to overcome, it’s a constraint to respect and work within. The challenge is how to build smart, secure paths around it that allow collaboration to move forward without compromise.
Explore how privacy-preserving technologies enable secure collaboration in constrained environments
