The safest way to bring AI into industrial operations.
Industrial AI must meet a higher security standard than consumer AI. DBR77 Vector was designed from the ground up for environments where data sovereignty, auditability, and operational safety are non-negotiable. No client data trains the model. Every deployment is versioned and auditable through the full CI/CD pipeline.
No training on client data · On-premise ready · GitHub Actions CI/CD · Docker isolation · SOC2/GDPR aligned
SECURITY ARCHITECTURE
Six pillars of industrial AI security.
Every aspect of DBR77 Vector — from training data to deployment to inference — is designed to satisfy enterprise security, legal, and OT requirements.
Your data never trains the model
DBR77 Vector does not use client production data, documents, or queries for model training. The training base consists exclusively of anonymized historical cases.
- No client data is used for training or fine-tuning
- Queries and outputs are not stored beyond the session
- Complete separation between inference and training pipelines
Deployment isolation by design
Every deployment model — on-premise, private API, or shared — is designed with isolation as a default, not an upgrade.
- On-premise: zero data leaves the client network
- Private API: dedicated compute, no multi-tenancy
- Shared API: enterprise security policies, session isolation
Human approval remains in the loop
Vector is an intelligence layer for decisions, not an autonomous authority. Every recommendation sits inside a human decision loop.
- AI advises, humans decide and approve
- Traceable recommendation logic for audit purposes
- No autonomous execution without explicit human authorization
Enterprise governance alignment
Vector is built to pass enterprise procurement, legal, and IT security reviews — not to work around them.
- SOC2, GDPR, and data residency alignment
- Audit-friendly outputs with source traceability
- Role-based access control and encryption at rest and in transit
Anonymized learning, not data harvesting
The model's continuous improvement is based on anonymized industrial patterns, not on copying or memorizing client-specific information.
- Pattern learning from transformation outcomes, not raw data
- No client identifiers, drawings, or proprietary figures in the training base
- Transparent methodology available for security review
Built for OT and IT convergence
Vector understands that industrial AI must satisfy both OT security requirements and IT governance frameworks simultaneously.
- Compatible with air-gapped and restricted network environments
- No mandatory external API calls during inference
- Designed for environments where uptime and safety are non-negotiable
COMPARISON
DBR77 Vector vs. public LLMs — security side by side.
Most public LLMs were not designed for industrial environments. Here is how Vector compares on the dimensions that matter most to enterprise security teams.
| Dimension | DBR77 Vector | Public LLM |
|---|---|---|
| Data used for training | Anonymized historical cases only. No client data used for training. | Trained on internet data. May use user inputs for improvement unless opted out. |
| Data residency | On-premise, private cloud, or region-specific hosting. Client chooses. | Data processed in provider's cloud infrastructure. Limited residency control. |
| Query and output storage | No persistent storage beyond the session. Client controls retention. | Queries may be logged, stored, and reviewed by the provider. |
| Network dependency | On-premise runs fully offline. Private API requires only internal network. | Requires internet connection to provider's servers for every query. |
| Multi-tenancy | On-premise and private API are single-tenant. Shared API uses session isolation. | Multi-tenant infrastructure shared across all users. |
| Procurement and compliance | Designed to pass enterprise security, legal, and OT reviews. | Often blocked or restricted by enterprise security policies. |
PIPELINE SECURITY
Auditable from code to inference.
Every step in the deployment pipeline is versioned, automated, and traceable.
Source Control
All model code, adapter weights, and deployment configs live in version-controlled repositories. Every change is tracked, reviewed, and attributable.
CI/CD via GitHub Actions
Automated pipeline: code push triggers Docker build, pushes to Docker Hub, deploys to RunPod. No manual steps. No untracked changes. Full audit trail.
Docker Isolation
The model runs inside an isolated Docker container. No shared state between sessions. No persistent storage of queries or outputs beyond the inference call.
Ready to see Vector in a secure environment?
Book a demo to see the full security architecture, or choose the deployment model that fits your requirements.