Ollama (Local LLMs) + Areebi

Areebi's integration with Ollama enables organisations to run open-source LLMs - including Llama 3, Mistral, CodeLlama, and any custom GGUF model - with full enterprise governance, entirely on local infrastructure. No data ever leaves your network. This makes the Ollama integration the ideal choice for air-gapped environments, data sovereignty requirements, and organisations that cannot send any data to external AI providers.

Ollama makes it simple to run powerful open-source models on commodity hardware, but running models locally does not automatically mean running them securely. Without governance, employees can still input sensitive data into local models, with no audit trail and no policy enforcement. Areebi solves this by wrapping Ollama with the same DLP engine, audit logging, and policy builder controls that apply to cloud-hosted providers - ensuring that local AI usage is just as governed as cloud AI usage.

The integration connects to one or more Ollama instances running on your infrastructure. Areebi manages model availability, user access, and governance policies centrally. Users interact with local models through Areebi's workspace interface, and administrators maintain visibility into all usage without any data leaving the organisation's network boundary.

Even when models run entirely on-premise, governance is essential. The DLP engine scans every prompt before it reaches the local model, applying the same 50+ built-in detectors for PII, PHI, financial data, and custom patterns that apply to cloud providers. This protects against data exposure to local model logs, model fine-tuning datasets, and shared infrastructure where multiple teams may access the same Ollama instance.

Audit logging captures every interaction with local models: user identity, workspace, model name, token count, and conversation content. For organisations pursuing SOC 2 or HIPAA compliance, these logs demonstrate that AI usage is monitored and controlled - even when no external APIs are involved. Auditors increasingly expect governance over all AI tools, not just cloud-hosted ones.

Policy enforcement allows administrators to control which models are available to which user groups. Sensitive departments can be restricted to specific model versions, token budgets can prevent resource monopolisation on shared GPU infrastructure, and acceptable use policies are enforced automatically. The policy builder provides the same granular controls for Ollama as for any cloud provider, maintaining a consistent governance posture across your entire AI stack.

Data sovereignty regulations in many jurisdictions require that certain categories of data never leave the country or organisation's infrastructure. The Ollama integration addresses this requirement directly: models run locally, prompts are processed locally, and Areebi's governance engine operates within your network. There is zero external data transmission at any point in the workflow.

For healthcare organisations, the combination of local model execution and Areebi's PHI masking means HIPAA compliance does not require a Business Associate Agreement with an external AI provider - because no external provider is involved. For financial services, local deployment eliminates concerns about third-party data access while maintaining the audit trail regulators expect.

Workspace isolation ensures that different departments using the same Ollama infrastructure cannot see each other's conversations or data. Visit the trust centre for architecture documentation, review pricing for on-premise deployments, or request a demo to see air-gapped AI governance in action.