Enterprise private LLM for workflows that cannot leave the perimeter.
For specific high-sensitivity workflows, like clinical documentation, MNPI-heavy research, regulated finance reviews, pre-filing patent prosecution, and contracts under negotiation, LogosGuard supports an enterprise private LLM deployment. Models run inside your VPC or on-prem. Prompts and outputs never leave the company environment. The same LogosGuard policy engine and audit log apply on top.
Private LLM is the strongest AI control. It is also the longest to deploy and the most operationally demanding. Most organizations reserve it for specific workflows and use the enterprise browser extension and desktop app for general AI usage.
What "private LLM" means at LogosGuard.
An enterprise private LLM deployment runs frontier-class language models inside your VPC, on-premises, or in a customer-controlled Azure tenant. The defining property is that prompts and outputs do not leave the customer environment. The LogosGuard policy engine layers on top of the model, applying the same data-class detectors, the same warn / redact / block / allow actions, and the same audit log shape as the enterprise browser extension and desktop app, but with the additional guarantee that even on submission, no content reaches an external AI vendor.
When to scope a private LLM.
- Clinical documentation: workflows handling full PHI under HIPAA, where even an external vendor path is too much risk.
- MNPI-heavy research: investment-banking and asset-manager workflows where deal documents and pre-announcement information are the point of the prompt.
- Regulated finance reviews: credit, surveillance, and compliance reviews subject to FINRA / SEC documentation rules.
- Pre-filing patent prosecution: patent counsel drafting claim language with unfiled application content.
- Contracts under negotiation: counterparty-confidential terms in active deal work.
- Customer-contractual restrictions: contracts that explicitly prohibit transmission of certain content to external AI providers.
How a private LLM deployment looks.
- 1
Model selection
Pick the model class (Llama, Qwen, Mistral, or other) and size that matches your workflow. Hosted in your environment via Modal, your own GPU infrastructure, or a customer-controlled cloud tenant.
- 2
Infrastructure hosting
VPC deployment with appropriate isolation, separate subnets, private endpoints, no internet egress for model traffic. On-prem deployments use the same model artifacts on your hardware.
- 3
Policy engine layered on top
The LogosGuard policy engine runs in front of the private model with the same warn / redact / block / allow logic. A redaction rule that fires on the enterprise browser extension fires on the private LLM identically.
- 4
Audit log integration
Events flow to the same audit log shape as the rest of the LogosGuard deployment. Logs export to your SIEM and surveillance pipelines without retaining underlying content.
Why private LLM is not the default.
Private LLM is the strongest control LogosGuard offers, and also the longest to deploy and the most operationally demanding. It requires GPU capacity in your environment, model selection and tuning, and a longer evaluation cycle. The enterprise browser extension is days; the enterprise desktop app is weeks; the enterprise private LLM is months.
Most organizations do not deploy a private LLM company-wide. They reserve it for specific high-sensitivity workflows and use the enterprise browser extension and desktop app for general AI usage. The pattern is to scope private LLM to specific teams or matter codes, with strict policy that hard-blocks the rest of the organization from accessing it.
What stays inside your environment.
- Prompts: every prompt sent to the private model.
- Outputs: every model response.
- Model state: weights, fine-tuning artifacts, embeddings.
- Fine-tuning data: any company-specific training data used to specialize the model.
- Embeddings: vector representations of company content for retrieval-augmented workflows.
- Audit metadata: flows to your SIEM in formats you control.
Pair with browser extension and desktop app.
Most LogosGuard deployments combine all three enterprise surfaces. The enterprise browser extension covers the majority of AI usage. The enterprise desktop app extends to native AI desktop clients and command-line AI tools. The enterprise private LLM is reserved for specific high-sensitivity workflows that should never touch external AI vendors. The policy engine and audit log are unified across all three, so a security team has one console, one set of rules, and one event stream.
Deployment by data class.
| Data class | Browser extension | Desktop app | Private LLM |
|---|---|---|---|
| General PII (most workflows) | Redact | Redact | - |
| Customer support PII | Redact | - | - |
| Source code (general) | Warn | Warn | - |
| Embedded credentials | Hard block | Hard block | - |
| PHI in clinical documentation | Block | Block | Allow |
| MNPI in research workflows | Block | Block | Allow |
| Privileged litigation work product | Block | Block | Allow |
| Pre-filing patent claims | Block | Block | Allow |