Private AI
AI deployed on infrastructure the client controls (on-premise, in the client's cloud tenancy, or air-gapped), with no third-party LLM provider in the data path and no inference-time data export.
How it works
Private AI is the architecture for any workload where customer, patient, or engineering IP data cannot leave the controller. The model runs inside the client's tenancy. Inference happens on infrastructure the client controls. The audit trail is in the client's own logging stack. No third-party LLM provider sees the content. Implementation patterns include open-weights models (Llama 3, Qwen, Mistral) deployed on the client's GPU infrastructure or self-hosted inference clusters, RAG systems running against private vector databases, and frontier API models accessed only through controlled gateways with PII redaction at the boundary. For UK regulated firms this is not a preference; it is the architecture the FCA, the SRA, the ICO, and NHS DSPT expect. Ayoob AI builds private AI as the default architecture.
Related terms
On-Premise AI
AI deployed on hardware the client owns and operates inside their own data centre or office facility, with no dependency on external cloud or model providers for inference.
Air-Gapped AI
AI deployed inside a network that has no connection to the public internet, used for the most security-sensitive workloads where any external connectivity is prohibited.
Cloud LLM
A language model accessed via a third-party provider's API (OpenAI, Anthropic, Google, others), where inference happens on the provider's infrastructure and content is sent to the provider for processing.
Data Residency
The geographic location where data is stored and processed, with regulatory requirements (UK GDPR, sector-specific rules) often constraining where personal or regulated data can travel.
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