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Powering AI at the Tactical Edge with Hybrid Multi-Cloud
3 min read
Artificial intelligence (AI) is transforming defense operations, but deploying AI at the tactical edge presents unique challenges. A recent Cyber Defense Magazine article highlights how AI workloads are moving closer to the battlefield, where real-time decision-making is critical, and connectivity to centralized systems may be limited.
While AI promises faster insights and smarter autonomous systems, it requires low-latency, high-performance compute resources that can operate securely in contested or remote environments. Traditional defense IT systems struggle to provide the necessary speed, scalability, and compliance required for edge AI deployments. Similarly, commercial cloud platforms alone cannot meet strict security requirements, creating a gap between capability and operational need.
The tactical edge includes systems operating close to the point of action, including unmanned platforms, sensors, mobile command centers, and autonomous vehicles. AI deployed in these environments enables real-time processing of sensor and operational data, autonomous or semi-autonomous decision-making, reduced dependence on centralized systems, and faster responses in contested or disconnected conditions.
Unlike centralized AI deployments, tactical edge environments demand low-latency compute, high reliability, and strong security controls, often under constrained or intermittent network conditions.
AI workloads are inherently compute-intensive. Model training and large-scale analytics benefit from elastic cloud resources, while inference and real-time processing must run close to the mission. Relying exclusively on on-premises systems limits scalability and agility, while cloud-only approaches struggle to meet security, compliance, and availability requirements at the edge.
As highlighted in Cyber Defense Magazine, this creates an operational gap: AI must run closer to the mission, but infrastructure is often centralized and inflexible.
Hybrid multi-cloud architectures address this gap by allowing workloads to run across cloud, on-premises, and edge environments, based on operational requirements.
For tactical edge AI, hybrid multi-cloud enables cloud-based training and model development using scalable compute, edge-based inference for low-latency, real-time decision-making, secure data segmentation aligned with classification and compliance needs, and operational resilience even with limited or disrupted connectivity.
Defense organizations operate under strict regulatory and security requirements, including Authority to Operate (ATO) processes, cybersecurity mandates, and data classification constraints. Legacy infrastructure and manual deployment workflows often slow AI adoption and modernization efforts.
Hybrid multi-cloud supports a more flexible model by enabling policy-driven workload placement, separation of sensitive and non-sensitive data, auditable operations across environments, and faster testing, deployment, and scaling of AI models.
While tactical edge AI is often discussed in a defense context, the same architectural challenges apply across civilian government and commercial sectors. Organizations running AI in distributed or remote environments face similar constraints around latency, availability, and security.
Common use cases include emergency response and disaster recovery, critical infrastructure and energy operations, manufacturing and industrial IoT, and distributed research and analytics workflows.
AI at the tactical edge is no longer experimental. It is operationally essential. As Cyber Defense Magazine highlights, delivering intelligence where decisions are made requires infrastructure that can operate securely, reliably, and at scale across distributed environments.
Hybrid multi-cloud provides the architectural foundation to support this shift, enabling AI workloads to span cloud, on-premises, and edge systems. When paired with intelligent orchestration, it allows organizations to deploy AI that remains resilient, compliant, and mission-ready, regardless of location or connectivity.
For organizations operating in complex, distributed environments, the message is clear: modern AI requires infrastructure designed for the edge.