KoboldCPP + Areebi

KoboldCPP is a lightweight, CPU-optimised inference engine for running GGUF and GGML models on hardware that lacks dedicated GPUs. It runs on virtually any machine - from older office workstations to embedded systems in field environments - making it the tool of choice for organisations that need local AI capabilities where GPU infrastructure is unavailable, impractical, or prohibited. Areebi's integration with KoboldCPP brings full enterprise governance to these resource-constrained deployments without adding significant computational overhead.

The environments where KoboldCPP is most valuable are often the environments where governance is hardest to implement. Air-gapped facilities, remote field offices, classified networks, and edge deployments typically lack the infrastructure for traditional cloud-based security tooling. Yet these are precisely the environments where shadow AI risk is highest: personnel with access to sensitive data deploy KoboldCPP on available hardware, load a quantised model, and begin processing classified, proprietary, or regulated information with zero oversight. Areebi addresses this by providing a governance layer that is as lightweight and self-contained as KoboldCPP itself.

The integration connects Areebi's governance engine to KoboldCPP's API server, applying DLP scanning, access controls, and audit logging to every interaction. Because both Areebi and KoboldCPP are designed for constrained environments, the combined footprint remains minimal - no GPU required, no cloud connectivity required, and no significant additional RAM or CPU overhead. This makes governed AI accessible in deployment scenarios where other solutions simply cannot operate.

Air-gapped environments present unique governance challenges. There is no cloud SIEM to stream logs to, no external API for DLP updates, and no network path for centralised policy management. Areebi's KoboldCPP integration is purpose-built for these constraints. The DLP engine operates entirely offline with locally stored detection rules, audit logs are written to local encrypted storage with configurable retention, and policies are deployed via secure transfer mechanisms that align with air-gap protocols. When connectivity is periodically available, logs can be batch-exported for centralised analysis.

In resource-constrained deployments, governance overhead must be minimal. Areebi's DLP scanning adds negligible latency to KoboldCPP's already-efficient CPU inference pipeline - typically under 20ms per prompt on hardware that runs KoboldCPP. Policy evaluation is lightweight and deterministic, audit logging uses append-only flat files that impose minimal I/O overhead, and the entire governance layer runs within a memory footprint measured in tens of megabytes. This efficiency means that governance does not compete with model inference for the limited CPU and RAM available on constrained hardware.

Compliance frameworks do not exempt air-gapped or disconnected environments. SOC 2 requires evidence of access controls and monitoring for all systems that process organisational data, regardless of network connectivity. HIPAA requires audit trails for systems that handle protected health information, even on isolated networks. Defence and intelligence frameworks impose even stricter requirements for classified environments. KoboldCPP deployments in these settings need governance that works within the connectivity and resource constraints of the environment - and Areebi is designed for exactly this scenario.

Areebi generates compliance artefacts locally: audit logs with cryptographic integrity verification, DLP scan results with timestamps and user attribution, policy enforcement records, and access control logs. These artefacts satisfy auditor requirements whether they are reviewed on-site in a classified facility or exported via approved channels to a centralised compliance system. For organisations operating under multiple regulatory frameworks simultaneously - common in defence health or financial intelligence contexts - Areebi's unified governance model ensures that a single deployment satisfies all applicable requirements without duplicating infrastructure or administrative effort.

See how the Areebi platform governs AI across every deployment model, review the trust centre for air-gapped architecture documentation, explore pricing for edge and constrained deployments, or request a demo to evaluate governance for your specific environment.