TrueFoundry + Areebi

TrueFoundry is a Kubernetes-native LLMOps platform that handles the full lifecycle of ML model deployment: from fine-tuning and experimentation through to production inference serving and scaling. It abstracts away the complexity of Kubernetes for ML teams, providing a developer-friendly interface for deploying, monitoring, and scaling LLM workloads. Areebi integrates with TrueFoundry to add the governance layer that MLOps platforms inherently lack - because TrueFoundry is designed to move models from development to production quickly, it does not include the data loss prevention, compliance logging, and policy enforcement that enterprise security teams require before those models serve real users.

The governance gap in MLOps platforms like TrueFoundry is structural, not a product deficiency. TrueFoundry excels at infrastructure orchestration - scheduling GPU workloads on Kubernetes, managing model artefacts, autoscaling inference endpoints. But the question of whether a user's prompt to a TrueFoundry-served model contains regulated data, or whether a particular team should have access to a particular fine-tuned model, sits outside the MLOps domain. Areebi fills this by governing the inference endpoints that TrueFoundry creates, applying DLP scanning, access controls, and audit logging at the point where users interact with deployed models.

For organisations using TrueFoundry to manage their internal LLM infrastructure, Areebi provides a single governance plane across all deployed models. Whether a model was fine-tuned on TrueFoundry and served via its inference API, or deployed as a custom container on TrueFoundry's Kubernetes clusters, Areebi's policies apply uniformly. Administrators configure governance rules once in the Areebi policy builder, and those rules cover every TrueFoundry endpoint - eliminating the model-by-model governance configuration that would otherwise be required as the ML team deploys new models.

TrueFoundry deployments typically involve multiple models across multiple Kubernetes namespaces, each potentially serving different business units. Areebi's governance layer maps to this structure: policies can be scoped to individual TrueFoundry endpoints, Kubernetes namespaces, or model categories, giving administrators granular control without requiring a flat, one-size-fits-all policy. The DLP engine inspects every inference request to TrueFoundry-served models, applying the same 50+ PII detectors that protect cloud-hosted model calls. For fine-tuned models that may have been trained on sensitive internal data, DLP on the response side is equally critical - Areebi scans model outputs for regurgitated training data, a known risk with fine-tuned LLMs that TrueFoundry's platform does not monitor.

Lifecycle governance is where Areebi's TrueFoundry integration goes beyond point-of-inference controls. Areebi logs fine-tuning data inputs when training jobs are initiated through TrueFoundry, creating an audit trail that connects a deployed model to its training provenance. When a model is promoted from staging to production on TrueFoundry, Areebi can enforce a policy gate - requiring that compliance checks have passed before the model is exposed to end users. This model-promotion governance is essential for organisations operating under SOC 2 change management controls, where moving a model to production is a controlled change that requires documentation and approval.

Organisations adopting TrueFoundry typically run it on their own Kubernetes infrastructure (on-premises or in their cloud tenancy), which means model inference stays within their data boundary. This is a strong foundation for compliance, but infrastructure-level data residency does not equal compliance. Auditors examining AI usage under HIPAA, SOC 2, or financial regulations need evidence of who accessed which model, what data was sent, what was returned, and what controls prevented sensitive data exposure. TrueFoundry's platform logging tracks infrastructure metrics - pod utilisation, latency, error rates - but not the content of inference interactions. Areebi provides the content-level audit trail: every prompt, response, DLP action, and policy decision is logged immutably and exportable to your compliance archive.

For organisations fine-tuning models on TrueFoundry with internal data, Areebi adds a governance dimension that is increasingly scrutinised by regulators: training data provenance. By logging what data enters fine-tuning pipelines and linking trained models to their data sources, Areebi creates the lineage documentation that emerging AI governance frameworks - including the EU AI Act and NIST AI RMF - are beginning to require. Combined with workspace isolation to segment access by business unit and role-based controls to limit who can deploy models to production, organisations get end-to-end governance of the ML lifecycle. Visit the trust centre for Areebi's security documentation, request a demo to see lifecycle governance in action, or review pricing for MLOps-scale deployments.