Private AI

Private AI for enterprise environments

Run AI workloads without exposing data to public AI platforms

Enterprises increasingly want to use AI to analyze, summarize, and augment internal data. At the same time, many organizations are explicitly prohibited from sending sensitive data to public AI services due to security, compliance, or contractual constraints.

Private AI addresses this by bringing AI workloads to the data, rather than exporting data to external platforms.

whitesky provides the infrastructure foundation to run AI workloads in a controlled, enterprise-governed environment.

The core enterprise AI problem: data leakage

Public AI platforms typically require:

  • sending prompts and data to external systems
  • reliance on opaque model training and retention policies
  • limited control over data residency and access paths

For enterprises, this introduces unacceptable risks:

  • unintentional disclosure of sensitive data
  • loss of auditability
  • uncertainty about data reuse or retention
  • violation of regulatory or contractual obligations

Private AI is not about model ownership — it is about keeping data inside the trust boundary.

AI as a workload, not a service dependency

whitesky treats AI as a workload class, not as a tightly coupled platform service.

This means:

  • AI workloads run on the same infrastructure principles as other enterprise workloads
  • compute, storage, and networking are explicitly governed
  • AI does not introduce hidden data paths or external dependencies

Models execute where the data resides.

Flexible GPU scaling with Drut

To support private AI workloads that require scalable compute without exposing sensitive data to public AI platforms, whitesky collaborates with Drut, a technology partner focused on flexible and efficient use of GPU resources.

This collaboration enables enterprises to allocate and scale GPU capacity dynamically for AI workloads running on whitesky, without statically dedicating GPUs to individual systems or workloads.

Key characteristics of this approach include:

  • Flexible GPU allocation
    GPU resources can be assigned to AI workloads based on actual demand, supporting both training and inference scenarios without long provisioning cycles.

  • Improved GPU utilization
    GPUs are used more efficiently across workloads, reducing idle capacity and avoiding overprovisioning.

  • Isolation and control
    GPU usage can be scoped per workload or environment, aligning with enterprise security, governance, and data isolation requirements.

  • Enterprise-controlled deployment
    GPU resources remain inside the enterprise trust boundary, supporting private AI use cases without reliance on public AI platforms.

This integration allows enterprises to run GPU-accelerated AI workloads on whitesky while maintaining control over data locality, access, and infrastructure usage.

Full control over data locality and access

With whitesky Private AI:

  • training data stays within your datacenter or designated cloud location
  • inference runs inside controlled environments
  • access is governed through enterprise identity and role models
  • network egress is explicitly defined and auditable

There is no implicit data flow to third-party AI services.

Isolation by design

AI workloads often require elevated compute resources (e.g., GPUs) and large datasets.

whitesky enforces isolation across:

  • tenants and environments
  • AI workloads and non-AI workloads
  • compute, storage, and networking layers

This prevents:

  • data cross-contamination
  • unintended lateral access
  • uncontrolled data exposure

Suitable for multiple enterprise AI use cases

Private AI on whitesky supports use cases such as:

  • internal document analysis and summarization
  • retrieval-augmented generation (RAG) on proprietary data
  • log and telemetry analysis
  • code and configuration analysis
  • domain-specific AI workloads requiring strict confidentiality

The platform does not prescribe how AI is used — it provides the boundary within which it can be used safely.

Integration with enterprise governance

Private AI must align with existing enterprise governance models.

whitesky enables:

  • identity-based access to AI workloads
  • separation between model operation and data ownership
  • auditable execution paths
  • integration with existing security and compliance frameworks

AI becomes an extension of enterprise IT, not a shadow system.

Infrastructure-first, model-agnostic

whitesky does not mandate specific AI models or frameworks.

Enterprises remain free to:

  • choose open-source or proprietary models
  • control model lifecycle and updates
  • evaluate performance and risk independently

This avoids vendor lock-in at the AI layer.

Delivery model: managed today, software tomorrow

whitesky delivers the Private AI infrastructure as a managed platform today, ensuring controlled deployment and predictable operations.

A software edition is rolling out in 2026, allowing enterprises or trusted partners to operate the same AI-capable platform independently if required.

The control model remains consistent across both delivery approaches.

Why enterprises use whitesky for Private AI

  • AI workloads run inside the enterprise trust boundary
  • no data leakage to public AI platforms
  • explicit control over data locality and access
  • isolation between AI and other workloads
  • alignment with enterprise governance and audit requirements

Next steps

  • Identify AI use cases involving sensitive or regulated data
  • Define data boundaries and access models
  • Design a private AI architecture with whitesky