AI behind the firewall: can you run your model entirely in-house?
Learn what it takes to run a language model and its search and document layer entirely inside your network, including updates, access controls, logging, backups, trade-offs and hybrid options.
Yes. You can run a language model and its search and document layer entirely inside your own network. The model, embeddings, documents and logs can all stay on-premises, without calls to external APIs. Doing this requires deliberate choices about hardware, updates, access and ongoing administration.
Running everything internally does not automatically make it secure. A misconfigured server, overly broad permissions or an unmanaged update can still expose information. You remain responsible for the whole chain: from obtaining model files to protecting backups and controlling who can query the system.
What does “entirely in-house” mean?
An environment is fully in-house only if every component that processes data stays within your network. That includes the language model that generates answers, the embedding model that turns text into searchable representations, document storage and the search index. The application, any supporting services and logging must also run internally.
Check that the application does not use an external API for embeddings, analytics, error reporting or telemetry. An interface may appear local while a service behind it still connects to an external provider. Map the data flows and test what happens when the server has no internet connection. If an essential component stops working, the setup is not fully self-contained.
This approach can suit documents you do not want to send to an external provider. See also when on-prem AI is worth the effort. The right setup depends on your security requirements, available hardware and the tasks the system needs to perform.
Obtaining models and applying updates safely
A system without direct internet access still needs models and security updates. You can bring files in through a controlled route, such as a separate computer with internet access. Before importing a file, check where it came from, whether it matches the intended source and whether it remained unchanged during transfer.
Keep a record of approved model files and configurations. Test each new version in an isolated environment with representative questions and documents. Check answer quality, access controls and application behavior. Move an update into production only after review, and keep a way to return to the previous working version.
An isolated network does not prevent outdated software from having vulnerabilities. Plan updates for the operating system, model server, search layer and application. If you import patches offline, treat the import itself as part of your security process: limit who can perform it and record what changed. For an overview of the hardware choices, see hardware for local AI.
Network, accounts and logging
Limit network access to the systems and people that need the service. Segmentation can stop the AI service from freely reaching other parts of the company network. Open only the necessary ports and assign appropriate permissions to administrator and user accounts. Use authentication and avoid shared accounts where individual access needs to be tracked.
An internal model can still reveal information to someone who is not allowed to see it. Tie document access to users or groups, and make sure those permissions also apply during retrieval. A filter that tries to remove documents only after generation may be inadequate if unauthorized text has already entered the model’s context.
Logs help with troubleshooting and investigations, but they can store sensitive content themselves. Decide what information you need, who can view the logs and how long to keep them. Technical details such as errors, timestamps and functions used may be more useful than storing complete questions and answers. If you do store content, limit it and protect the storage. Also consider encryption for stored data and connections within the network.
Backups and recovery are part of the setup
An in-house AI service consists of more than model files. You may also need documents, configurations, access rules, prompts, indexes and evaluation data to restore the service. Decide which parts you can rebuild and which you need to preserve. Test recovery periodically; a backup that has never been restored does not show that the recovery process works.
Encrypt backups and restrict access to them. A copy on the same server does not protect against every outage or unauthorized access. Record who can perform a restore and how you will check that the restored environment has the same access controls. The guide to backups for AI systems covers what to preserve alongside source documents.
What do you give up, and when does hybrid make sense?
An internal model gives you more control over where data is processed, but it may perform less well than an external model on some tasks. Quality depends on the model, hardware, language and use case. It may also be harder to get new capabilities quickly. Managing the system yourself takes time and requires knowledge of updates, monitoring and recovery.
A hybrid approach may work when sensitive documents must stay internal but you want to use an external service for other tasks. Deliberately separate the data flows: do not include confidential content in an external request, and document what information a service receives. Consider whether a sanitized or general question is enough. The appropriate boundary depends on your data, agreements and risk assessment.
Checklist for an in-house AI service
- Do the model, embeddings, document storage, search index, application and logs all stay within your network?
- Have external API calls, telemetry and unexpected connections been checked?
- Have model files been checked for provenance, with imports limited and recorded?
- Are updates tested, approved and reversible?
- Are network access, ports, accounts and document permissions limited to what is needed?
- Do logs contain no more sensitive content than necessary, and are storage and backups protected?
- Has recovery been tested, and is it clear who administers the service?
Running everything in-house is possible, but it requires ownership of every component and its lifecycle. Start by mapping data flows, users and administrative tasks. That gives you a basis for deciding whether local hosting fits your risks and whether you can maintain the environment securely over time.