TBW / AI solutions

Private AI

Keep sensitive workflows inside an intentional trust boundary.

Discuss your architecture

Private AI: Identity to Private network to Approved data to Retrieval

The business problem

Sending every prompt and document to a public endpoint may not fit enterprise data policy.

Unclear data and model boundaries
Disconnected systems and ownership
Production controls added too late

Explore AI security and governance →

What TBW provides

On-premises or private-cloud design

Designed around your environment, requirements and operating constraints.

Approved model access and retrieval

Designed around your environment, requirements and operating constraints.

Network segmentation and operating plan

Designed around your environment, requirements and operating constraints.

Architecture and workflow

Private AI workflow: Identity to Private network to Approved data to Retrieval

  1. Identity
  2. Private network
  3. Approved data
  4. Retrieval
  5. Model
  6. Audited answer

Architecture is selected after reviewing the actual data, identity, operations and integration boundaries. See enterprise integration →

A representative workflow

An analyst asks a question through an approved gateway; source documents remain in the private environment. This illustration describes a design pattern, not a deployed customer case study.

Explore controlled agent workflows →

Illustrative operator and approval gateway: Human operator to Approved gateway to Bounded action to Audit trail

Technologies involved

Technology categories below are examples for architecture discussion, not a vendor partnership or an assertion that every tool is deployed.

Private networkingidentity providersmodel servingencrypted storageaudit logs

Explore AI systems and infrastructure →

Implementation methodology

  1. Discover requirements
  2. Assess data and access
  3. Architect and prototype
  4. Integrate and secure
  5. Validate and operate

Consulting and training →

Security and governance

Classify data; authenticate users; apply least-privilege model and tool access; review retention and residency; test prompt injection and unsafe retrieval; log meaningful decisions; require human approval for sensitive actions. The controls and assurance required depend on your environment.

Review AI security →

Related services

Browse all AI solutions →

Frequently asked questions

Does private AI mean no cloud?

Not necessarily. A private-cloud deployment can still use cloud infrastructure; residency and provider access must be verified contractually and technically.

How are access and data boundaries defined?

Start with a data inventory, named owners and role-based permissions. Test the actual flow before production and revisit access as systems change.

What is the next step?

Bring a bounded use case, constraints and existing architecture to a consultation. No deployment or result is assumed before assessment.

Plan the next architecture decision

Discuss the workload, data boundaries, integrations and operational ownership with TBW.

Request a consultation

Consultation path: Requirements to Architecture to Review to Next steps

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