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You can use an open-weight or open-source AI model with confidential material only when you control the complete data path: the model runtime, prompts, retrieved documents, logs, tools, network connections, and host. Running inference on a workstation or inside your organization can remove a hosted provider from the path, but “open-source” and “local” are not privacy guarantees.
Start by classifying and minimizing the data, choose a defined deployment boundary, verify the model and software supply chain, lock down the inference service, restrict logs and tools, block unnecessary egress, and test what actually leaves the system. Local processing can still expose data through a connected application, a misconfigured API, a plugin, telemetry, a compromised host, or retained logs.
What “private” means when you run an AI model
For this purpose, privacy is a property of data flow, not a label on a model. A model may have publicly available weights or source code while an application sends prompts to a remote API. Conversely, a self-hosted model can keep inference traffic inside a company network, but the operator then owns security for the server, operating system, runtime, dependencies, storage, and integrations.
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Draw the path for every request before approving a deployment. Include:
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- The user prompt and any uploaded files.
- Retrieved documents, database results, embeddings, and conversation history.
- Generated output and intermediate tool results.
- Application, proxy, runtime, and audit logs.
- Model weights, adapters, tokenizers, containers, and package caches.
- Plugins, web search, code execution, connectors, and monitoring or support channels.
- Outbound traffic from the application, inference server, tools, and host.
“Local” is therefore something to verify. It does not mean that every component is offline, that logs are harmless, or that personal-data obligations disappear.
Does open-source AI keep data private?
No. Open licensing or downloadable weights say how software or model artifacts may be used; they do not specify where your prompts travel or who can access them. A locally installed interface might offer cloud models, web search, telemetry, automatic updates, remote support, or a fallback service. Any of those features can change the data path.
Ollama’s privacy policy, updated in March 2026, states that prompts, responses, and other content processed locally by its local software are not collected, stored, transmitted, or accessible to Ollama. The same policy distinguishes its cloud-hosted models, where prompts and responses are processed transiently to provide the service and are not used to train models. Treat this as a vendor policy statement for the specified modes, not as independent testing or a guarantee for another interface, integration, or runtime.
Use the same verification standard for any software: identify the mode in use, inspect its configuration, observe network connections, review logs, and test with non-sensitive data before introducing confidential material.
Choose the deployment boundary
The right choice depends on the sensitivity of the data, required capability, context length, concurrency, latency, available hardware, and the team that will operate the system. No single boundary is universally best.
| Deployment | Where data can travel | Connectivity and tools | Operational responsibility | Typical trade-off |
|---|---|---|---|---|
| Local workstation | Potentially limited to one device, but applications, extensions, logs, and the operating system remain in scope. | Can run without external connectivity; optional tools can reintroduce network paths. | One user or small team must secure the device, runtime, updates, storage, and access. | Simple to start, but limited by local memory, GPU capacity, concurrency, and endpoint security. |
| Organization-managed self-hosting | Prompts, documents, outputs, and logs can stay within a defined corporate network or data center. | Can be segmented or air-gapped; internal tools still need least-privilege controls. | The organization manages identity, patching, monitoring, incident response, backups, and decommissioning. | More control and capacity, with substantially greater engineering and operations work. |
| Hosted provider | Prompts, files, outputs, logs, and telemetry cross a provider boundary according to its contract and settings. | Usually requires external connectivity; provider-managed tools may be convenient but expand trust. | The provider operates much of the platform; you still configure access, retention, data handling, and integrations. | Often easier to scale, but unsuitable when policy forbids sending certain data outside your boundary. |
If external connectivity is forbidden, design an air-gapped deployment deliberately. Establish how model files, security updates, and packages enter the environment, how they are scanned and approved, and how the process is audited. An “offline” claim is credible only when the whole workflow, including updates and support, is offline or controlled.
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Prepare the data before it reaches the model
Classify the input
Define categories such as public, internal, confidential, personal, regulated, and credential or secret data. Map each category to an approved model boundary. A policy that says “do not paste sensitive information” is weaker than a rule enforced by application filters, access controls, and review.
Minimize every request
Remove fields the task does not need. Replace names, account numbers, exact addresses, tokens, and other identifiers with stable placeholders when identity is irrelevant. Send only the relevant document section instead of an entire repository. Minimization reduces what can appear in prompts, retrieved context, logs, outputs, backups, and error reports.
Use representative or synthetic data during development
Build prompts and evaluate workflows with synthetic or de-identified records whenever practical. Test edge cases without importing production secrets. Remember that privacy attacks can sometimes reveal information about people represented in training data; do not assume that an open model is incapable of memorization or extraction.
Build a controlled self-hosted stack
Use this sequence for a workstation, server, or private cluster. Record decisions so another operator can reproduce and audit them.
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Define the allowed boundary
Write down which devices, networks, accounts, storage systems, and tools may handle the data. Decide whether internet access is required, prohibited, or allowed only through an approved proxy. For an organization deployment, place the inference service on managed infrastructure in a documented network segment.
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Confirm the actual execution mode
Verify that the selected interface loads the model locally rather than routing requests to a cloud model. Disable cloud fallback, web search, remote plugins, and optional telemetry unless each is approved. Check configuration after upgrades because defaults and feature flags can change.
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Record and pin the supply chain
Inventory the model name and revision, weight files, tokenizer, runtime, libraries, container image, operating-system packages, acquisition source, and license. Store cryptographic hashes where your process supports them. Keep approved versions fixed rather than silently pulling a moving “latest” artifact.
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Validate model files before production
Acquire weights and datasets from trustworthy sources, scan and validate them, and document provenance. OWASP’s Large Language Model Security Verification Standard v2.0 recommends secure acquisition and storage and prefers safer formats such as SafeTensors over unsafe serialization such as Pickle where supported. Treat open-source frameworks and dependencies as software that still requires security assessment.
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Separate users and workloads
Require authenticated callers and authorize access by role, project, and data class. Segregate development, evaluation, and production instances. Scope service credentials to the minimum permissions, rotate them, and avoid sharing an administrator endpoint with ordinary inference traffic.
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Harden the serving endpoint
Do not expose inference or administrative APIs directly to an untrusted network. Put them behind an approved gateway or private network path, apply rate limits, cap request size and resource-intensive parameters, and maintain an inventory of every exposed listener. Keep the runtime, host, container images, and dependencies patched.
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Define log and artifact handling
Decide what is logged, why it is needed, who may read it, and how long it remains. Disable prompt and response capture when it is not required. Protect model registries, datasets, temporary files, intermediate outputs, and backups with access controls and encryption at rest. A local log can contain the same sensitive text as a prompt.
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Constrain tools and network egress
Enable only the tools required for the workflow. Isolate code execution, restrict filesystem mounts, and give connectors narrowly scoped credentials. Block unnecessary outbound and internal network access at the host, container, and network layers. vLLM’s security documentation warns that its example code-interpreter container inherits host Docker networking by default; that configuration can make internal services or cloud metadata reachable.
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Protect the model and software supply chain
Supply-chain compromise can expose data even when inference never leaves your network. A malicious or tampered model file, package, container, tokenizer, or dataset may alter outputs, execute code during loading, or create a path to secrets.
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- Prefer formats designed to avoid arbitrary code execution during deserialization, such as SafeTensors when available.
- Scan files and images before they enter production; do not load untrusted serialized objects on a privileged host.
- Keep a current inventory of deployed models, runtimes, libraries, containers, and hardware.
- Review licenses and usage restrictions before distributing a model or using it with customer data.
- Patch the host and serving stack, and reassess risk before changing model versions or enabling new integrations.
Secure logs, storage, and access
Privacy failures often occur outside the model process. Application traces, reverse-proxy access logs, notebook history, shell history, crash dumps, swap files, database backups, and observability tools may retain prompts or retrieved documents.
Choose retention periods based on an operational purpose, not convenience. Restrict access to operators who need it, separate security logs from content logs, and redact secrets before records reach a central system. Encrypt stored weights and datasets, protect backup keys, and securely delete retired copies according to your organization’s requirements.
Keep model administration separate from inference. An operator who can replace weights, alter system settings, or attach a tool can change the confidentiality properties of every request. Use multifactor authentication where available, review privileged access, and alert on unusual exports, configuration changes, or large request volumes.
Limit tools, retrieval, and outbound connections
A model can be local while its tools are not. Web search, email, ticketing, source-code access, databases, cloud storage, and code interpreters can transmit sensitive context or retrieve more data than the user intended.
- Allow-list tools by workflow; do not expose a general-purpose network client by default.
- Give each connector a separate, least-privilege identity and restrict which records it can read or modify.
- Use read-only access for retrieval when writing is unnecessary.
- Isolate code execution from the host and from internal services; restrict mounts, metadata endpoints, and outbound DNS or HTTP.
- Apply egress filtering and monitor denied and successful connections.
- Set limits on retrieved documents, tool calls, execution time, file size, and output volume.
Prompt-injection defenses are useful, but a system prompt is not an access-control boundary. Enforce permissions in the application, connector, operating system, and network.
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Test the real data path before using confidential data
Test the complete workflow with harmless canary values first. The objective is to observe behavior, not merely read a privacy statement.
- Inspect the runtime and application configuration for cloud modes, fallback providers, telemetry, remote updates, plugins, and support integrations.
- Place unique, non-sensitive markers in test prompts and retrieved documents.
- Monitor outbound network connections from the application, inference server, containers, and tools while processing the markers.
- Search application, proxy, runtime, crash, and observability logs to determine where the markers were retained.
- Exercise failure modes: unavailable model files, timeouts, oversized requests, authentication failures, and disabled network access. Confirm that errors do not send content to an unapproved service.
- Attempt access with accounts that should be denied. Verify tenant, project, document, and administrative separation.
- Test tool isolation, including attempts to reach internal services, cloud metadata, host files, or unauthorized databases.
- Repeat the checks after upgrades, configuration changes, and model swaps.
Keep the test record with the approved architecture. A one-time successful test does not prove that future updates, new integrations, or compromised credentials will preserve the same boundary.
Hardware and model sizing
Hardware depends on the model, quantization, context length, concurrency, and latency target. Vendor figures are examples for particular configurations, not universal requirements.
| Published example | Qualification | How to use it |
|---|---|---|
| 16 GB memory — Ollama, 2025 | Ollama reports that its 20B gpt-oss model can run on systems with as little as 16 GB memory in the described setup. | Use it as a starting reference for that quantized configuration, not as a requirement for every 20B model. |
| 80 GB GPU — Ollama, 2025 | Ollama reports that its 120B gpt-oss model can fit on a single 80 GB GPU in the described setup. | Confirm the exact quantization, context, and workload before purchasing hardware. |
| RTX 5090 test — Ollama, June 2026 | Ollama tested Gemma 4 26B with Q4_K_M quantization on an NVIDIA RTX 5090. | This is a concrete current hardware example, not a comparative recommendation or a required card. |
Benchmark your intended prompt lengths and simultaneous users on the exact model and quantization you will deploy. Leave capacity for the operating system, retrieval index, security tooling, and logs. A model that fits in memory may still be too slow, unstable under concurrency, or unable to support the context window your workflow needs.
Privacy and legal obligations still apply
Moving processing onto your own equipment does not automatically remove personal-data obligations. The Information Commissioner’s Office states: “It is important to note that local processing is not necessarily out of scope of data protection law.” Determine whether your use involves personal, confidential, regulated, or cross-border data, and apply the relevant retention, access, transparency, security, and incident-response requirements.
Document the purpose of processing, the categories of data, approved users, retention periods, suppliers and dependencies, and the controls that protect the system. Seek qualified legal or privacy advice for regulated workloads; technical locality alone is not a compliance determination.
Quick Recap
Common failure modes and fixes
| Symptom | Likely cause | Corrective action |
|---|---|---|
| The model runs locally, but firewall logs show outbound requests. | Cloud fallback, telemetry, updates, plugins, or a connected tool. | Identify the destination, disable or isolate the feature, apply egress controls, and retest with canary markers. |
| Only administrators can access the server, but many users can read prompts. | Verbose application or proxy logging. | Reduce content logging, redact secrets, restrict log access, and set a documented retention period. |
| A private model can read internal services. | Broad container networking, host mounts, or powerful connector credentials. | Isolate execution, remove unnecessary mounts, block internal routes, and issue narrowly scoped credentials. |
| A model update changes privacy behavior. | Unpinned runtime, image, model, or default configuration. | Pin versions, review release changes, run the data-path test suite, and approve the update before deployment. |
| Users paste secrets into a general assistant. | No classification, filtering, or approved-use guidance. | Enforce data classes in the application, minimize inputs, provide a safe workflow, and monitor for policy violations. |
Operational checklist
- Model, runtime, dependencies, containers, licenses, versions, and provenance are inventoried.
- Prompts and retrieved data are minimized and classified before inference.
- The execution mode is verified as local, private-network, air-gapped, or hosted according to policy.
- Inference and administration endpoints require authentication and authorization.
- Logs, temporary files, backups, model registries, and datasets have defined access and retention.
- Tools and connectors are allow-listed, least-privileged, and isolated.
- Outbound traffic is filtered and monitored; internal service and metadata access is blocked unless required.
- Non-sensitive canary tests verify network, logging, fallback, and failure behavior.
- Updates, configuration changes, and model swaps trigger another security review and test.
- Decommissioning removes model files, parameters, caches, logs, backups, and associated credentials securely.
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