Private & Sovereign AI
Private AI infrastructure for organisations that cannot send sensitive data to public APIs.
Designed for regulated enterprises, public sector, financial services, healthcare, and any organisation with strict data-residency, compliance, or sovereignty requirements.
Capabilities
What private AI actually requires.
Local LLM serving
- ·On-prem inference
- ·Private cloud deployment
- ·Sovereign cloud patterns
- ·Hybrid edge / core
Internal AI agents
- ·Agent platforms
- ·Tool-call governance
- ·Policy gates
- ·Action audit
Secure RAG
- ·Private vector stores
- ·Access-scoped retrieval
- ·Source attribution
- ·Document permissions
Data isolation
- ·Tenant boundaries
- ·Network segmentation
- ·Encryption at rest / in transit
- ·Key management
PII controls
- ·PII scanning
- ·Redaction
- ·Retention policy
- ·Subject-access tooling
Auditability
- ·Immutable audit logs
- ·Prompt and response capture
- ·Decision tracing
- ·SIEM integration
Policy enforcement
- ·Tool permissioning
- ·Model allow-lists
- ·Output policies
- ·Rate and quota controls
Deployment patterns
- ·On-prem
- ·Private cloud
- ·Sovereign cloud
- ·Hybrid
- ·Air-gapped / restricted-network
compliance-sensitive
Compliance-aligned, not compliance-theatre.
Private AI is more than self-hosting a model. It is a coherent platform that can be audited, isolated per tenant, and reasoned about by security, legal, and risk teams.
Engagements include control mapping to SOC 2, ISO 27001, and GDPR; data-flow diagrams; policy enforcement points; and a deployment model that fits the operating environment — on-prem, private cloud, sovereign cloud, hybrid, or restricted-network.
The result is a model platform your security and compliance teams will actually approve.
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