Local inference. No network signal. Hundreds of abliterated models One ollama pull away

Local models are already inside.
See them. Control them.

Employees run local models and uncensored fine-tunes on work machines. Network controls see none of it: no prompt, no model, no output.

Certiv brings AI Agent Assurance to the endpoint with Certiv Scout.

Book a Demo See how Certiv finds local models
In short

Securing a local LLM means finding and governing models that run entirely on company devices. Certiv Scout inventories those models, flags risky ones, and enforces policy before actions run.

The risk

Shadow local AI on the machines you manage

With local models, the vendor is out of the loop. Security cannot see what’s running, what data goes in, or what happens next.

Installed in minutes

An employee can install Ollama or LM Studio, download a model from Hugging Face, and run it within minutes. No account, API key, or connection to an AI vendor needed.

Models with refusals removed

An abliterated model has had the refusal direction removed from its weights. With uncensored fine-tunes, similar behavior is removed during training. These models can answer requests for malware, phishing kits, and exfiltration scripts.

Corporate data leaves no trace

Sensitive data pasted into a local prompt stays on the laptop. No vendor log, network event, or DLP alert records it. Security has no record of the prompt or output.

Agents without a safety layer

Local runtimes expose an OpenAI-compatible endpoint on localhost. AI agents can use that endpoint to generate and execute tool calls without a vendor safety layer. It all happens on the device.

Unvetted files execute locally

A model package may contain pickle files or custom code that executes when the model loads. Both the publisher and the file’s origin may be unknown. A single model download can open a path to local code execution.

Why your stack misses it

Local inference never crosses the network

The current security stack watches network traffic, cloud services, and known malicious behavior. Local inference produces none of those signals.

CASB / SWG

CASB and SWG inspect traffic bound for services like chatgpt.com. Prompts passed between two local processes never reach them.

DLP

DLP watches for sensitive data moving through monitored channels. Data pasted into a local prompt stays on the device, so there is no DLP event.

LLM gateways and API proxies

Gateways govern vendor APIs, issued keys, and requests routed through them. A localhost endpoint uses none of those. Its prompts and outputs bypass the gateway.

EDR

To EDR, a signed process like ollama.exe is using CPU, GPU, memory, and disk. EDR cannot identify the loaded model or see its prompts and outputs.

What Certiv does

The control that lives where the models live

Endpoint control sees the processes, models, and actions on the device that network tools cannot. Certiv Scout uses those signals to build a live fleet-wide inventory and enforce policy.

01

Endpoint discovery

Certiv Scout finds AI agents and local model runtimes on every endpoint, including Ollama, LM Studio, llama.cpp servers, and other local services. Security gets one live inventory across the fleet.

02

Model identification

Scout shows the models on each device. It flags abliterated models and uncensored fine-tunes for review, so security can separate approved models from unknown or prohibited files.

03

Pre-execution policy

Scout checks policy before an AI agent action runs. Based on the action’s impact, it can block it, allow it, or escalate it. Each decision and action is recorded in a full audit trail.

04

Human approval

High-impact actions can be held for a designated reviewer. Nothing runs until the reviewer sees the request and approves or denies it.

FAQ

Local LLM security questions

Expand to view common questions.

What is an abliterated model?
An abliterated model is an open-weight model whose refusal direction has been removed from its weights. That weakens or strips out the behavior used to reject harmful requests. The model may then answer requests for malware, phishing kits, exfiltration scripts, and other harmful content.
How do I detect local LLMs on company devices?
Use endpoint discovery to find local runtime processes, services, listening ports, and model files. Network monitoring cannot detect inference that never leaves the device. Certiv Scout reports the local runtimes and installed models across your fleet.
Is Ollama safe for enterprise use?
Ollama can be used in an enterprise with endpoint policy, approved models, access controls, and audit logging. By itself, though, it makes pulling and serving local models easy. Security teams still need to see every runtime, installed model, and AI agent action.
Can DLP or a firewall see local LLM usage?
Not when the prompts and outputs stay on the device. DLP and firewalls inspect monitored data flows. Local inference can pass data entirely between local processes. They might see the model download, but not the prompts, outputs, or actions that follow.
Should we ban local models?
No. Approve the local models that meet company policy and monitor how they are used on managed devices. Block unknown and abliterated models. Let sanctioned ones run under policy.
How does Certiv detect and control local models?
Certiv Scout runs on every endpoint, finding local runtimes, installed models, and connected AI agents. It flags risky models and applies policy before an action runs. Scout can allow the action, block it, or hold it for human review. The outcome goes in the audit trail.

Keep local models available.
Keep them under policy.

See every local model. Stop risky AI agent actions before they run.

Book a Demo

Keep reading

Product overview

Discover shadow AI agents, understand what they do, and enforce policy before risky actions happen.

Certiv vs AI proxies

Why network proxies miss the point of intent, local tools, endpoint context, and local-model agents.

For security teams

The controls to evaluate when governing AI agents and local models across an organization.

What is AI Agent Assurance?

The category behind the controls on this page: authorize every agent action before it executes.

Pre-execution policy enforcement

Every agent action checked against enterprise policy before it executes, on hosted and local models alike.