Access and purpose
Authenticated APIs, role- and purpose-based authorization and inherited source permissions govern each request.
Platform · Private AI Core
Private Hosting Is the Start. Governance Makes It Trustworthy.
Defense-in-depth controls minimize evidence, preserve permissions, constrain authority, validate output and record decisions.
Local hosting reduces external exposure, but location alone does not guarantee privacy, security or compliance.
Design-partner roadmap This build contains no model runtime and calls no model — every model-shaped capability reports an explicit “model not configured” state. The architecture, model classes and hardware figures on this page are intended design and sizing targets: not measured results, and not a supported production specification.
There is no direct model-to-production authority path. Every consequential action remains behind explicit policy and authorization.
Customer-controlled trust boundary
The model proposes. Controls validate. People decide.
- Minimize
Select relevant evidence, redact secrets and enforce purpose before reasoning.
- Propose
The approved model returns a structured explanation or recommendation.
- Validate
Schemas, deterministic controls and policy services treat the output as untrusted input.
- Authorize
An authorized person reviews consequential recommendations.
- Act and audit
Only an approved workflow acts; evidence, versions and decisions are recorded.
Data controls
Minimize before reasoning
Private inference should not receive credentials, unrestricted repositories or every raw log line.
Evidence handling
Redaction, size limits, secrets exclusion, configurable retention and segregated knowledge collections reduce exposure.
Network and storage
No direct internet by default, TLS in transit, encryption where required and local telemetry support restricted operation.
AI-specific defense
Retrieval and fine-tuning do not remove prompt-injection risk
Retrieved content and model output are untrusted inputs, even when every service runs locally.
Content isolation
Instructions and retrieved evidence are separated, bounded and checked for injection or poisoned knowledge.
Structured validation
Output schemas, allowlisted operations, size limits and deterministic checks constrain downstream use.
No unrestricted tools
System prompts are neither secrets nor authorization; the model receives no standing production authority.
Supply chain
Know exactly what is running
Models are software artifacts with licenses, provenance, dependencies and change risk.
Verified artifacts
Model and software artifacts are signed or hash-verified and accompanied by bills of materials.
Versioned configuration
Prompts, adapters, quantization, inference engine and retrieval indexes are approved as a release.
Controlled updates
Offline-capable packages, acceptance tests and rollback artifacts support governed change management.
Audit and assurance
Make assistance reviewable
Audit scope and retention are customer decisions, but every deployment needs enough lineage to investigate a result.
Decision lineage
Record the requesting identity, task purpose, evidence references, model release, retrieved sources, response and final decision.
Human override
Authorized reviewers can reject recommendations, document corrections and trigger re-evaluation or rollback.
Recognized guidance
Controls are informed by NIST AI RMF and its Generative AI Profile and by OWASP LLM risk guidance; this is not a certification claim.
Operational boundaries
What the Private AI Core does not claim
Clear limits protect buyers from confusing model capability with product authority or complete security coverage.
No correctness guarantee
Outputs can be incomplete or wrong and require evidence grounding, validation and review.
No tool replacement
It does not replace vulnerability scanners, antivirus, EDR, SIEM, access controls or security analysts.
No automatic training
Customer data is not automatically absorbed into model weights; approved retrieval or adaptation is separately governed.
No blanket compliance promise
A local model alone does not establish privacy, regulatory compliance or commercial interchangeability.
Platform · Private AI Core
Size the system around your evidence and workload
The assessment defines the deployment class, model class, tested capacity envelope, availability design and implementation scope before hardware is committed.