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.

Public AI versus private AIOn a public AI platform, prompts and data leave the enterprise trust boundary for an external AI service, with unclear retention and reuse and loss of auditability. With private AI on whitesky, the data and the AI workload with its model and GPU both sit inside the trust boundary: training and inference stay inside, access is identity-based, the workload is isolated, and egress is explicit and auditable.Public AI platformYour trust boundaryYour dataprompts anddata leaveExternal AI service✕unclear retention and reuse✕loss of auditabilityPrivate AI on whiteskyYour trust boundaryYour dataAI workloadmodel + GPU✓training and inference inside✓identity-based access✓isolated from other workloads✓egress explicit and auditableNo implicit data flow tothird-party AI services
Public AI versus private AIOn a public AI platform, prompts and data leave the enterprise trust boundary for an external AI service, with unclear retention and reuse and loss of auditability. With private AI on whitesky, the data and the AI workload with its model and GPU both sit inside the trust boundary: training and inference stay inside, access is identity-based, the workload is isolated, and egress is explicit and auditable.Public AI platformYour trust boundaryYour dataprompts anddata leaveExternal AI service✕unclear retention and reuse✕loss of auditabilityPrivate AI on whiteskyYour trust boundaryYour dataAI workloadmodel + GPU✓training and inference inside✓identity-based access✓isolated from other workloads✓egress explicit and auditableNo implicit data flow tothird-party AI services
Public AI sends your data out; private AI brings the workload to the data, inside your 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.

Static versus flexible GPU allocationLeft: GPUs statically dedicated to individual systems, where some sit idle while held back for one workload. Right: with Drut, GPUs form shared capacity that is assigned to training and inference workloads based on actual demand, with free GPUs available to the next workload, GPU use scoped per workload, and all GPUs inside the enterprise trust boundary.Statically dedicatedGPUs fixed to one system eachWorkload AGPUGPUGPUWorkload BGPUGPUGPUWorkload CGPUGPUGPUin useidle, held backFlexible allocationwith Drut, based on actual demandShared GPU capacityGPUGPUGPUGPUGPUGPUGPUGPUGPUtraininginferencefree✓GPU use scoped per workload✓Less idle capacity, no overprovisioning, GPUs stay inside your trust boundary
Static versus flexible GPU allocationLeft: GPUs statically dedicated to individual systems, where some sit idle while held back for one workload. Right: with Drut, GPUs form shared capacity that is assigned to training and inference workloads based on actual demand, with free GPUs available to the next workload, GPU use scoped per workload, and all GPUs inside the enterprise trust boundary.Statically dedicatedGPUs fixed to one system eachWorkload AGPUGPUGPUWorkload BGPUGPUGPUWorkload CGPUGPUGPUin useidle, held backFlexible allocationwith Drut, based on actual demandShared GPU capacityGPUGPUGPUGPUGPUGPUGPUGPUGPUtraininginferencefree✓GPU use scoped per workload✓Less idle capacity, no overprovisioning,GPUs stay inside your trust boundary
Instead of GPUs fixed to single systems, capacity is assigned to AI workloads as demand requires. GPU counts are illustrative.

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 service

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

A software edition for self-operation is planned, at the earliest in 2027.

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