Why on-premise AI is the only option for regulated industries
Cloud AI introduces risks that regulated organisations cannot accept. Here is why local inference is not a compromise, it is an advantage.
For organisations in energy, defence, government, and critical infrastructure, sending proprietary data to a third-party cloud API is not an option. Regulatory frameworks, security policies, and operational requirements all point in the same direction: if you want AI, it has to run on your infrastructure.
The problem with cloud AI in regulated environments
Cloud-based AI services require data to leave your network. Even with encryption in transit, the data is processed on hardware you do not control, in jurisdictions you may not approve, by models that may retain information for training purposes.
For many organisations, this is a non-starter:
- Energy operators handle grid data, maintenance records, and safety procedures that fall under national security regulations
- Defence and government work with classified or restricted material that cannot leave sovereign infrastructure
- Healthcare and pharma are bound by GDPR, HIPAA, and sector-specific data handling requirements
Local inference changes the equation
Running AI models on-premise eliminates the entire class of cloud-related risks. There is no data in transit to intercept, no third-party processing agreement to negotiate, and no model provider that might change their terms of service.
With purpose-built hardware like WaveNode, local inference is no longer a research project. It is production-ready infrastructure that fits in a standard server rack.
What this means for WaveDoc
WaveDoc is designed from the ground up for on-premise deployment. Every query, every answer, every document citation is processed locally. The audit trail stays on your hardware. The models never phone home.
This is not a limitation. For regulated industries, this is the only architecture that makes sense.