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You can ask AI questions about a legal document without sending its text to a cloud inference service by running a model on a computer or server you control. The key is to keep the entire document-processing path local—not just the model—and to verify the software’s network, logging, and access behavior before using client material. Local processing can reduce exposure to a cloud provider, but it does not by itself make a workflow confidential, secure, lawful, or accurate enough for legal work.
What “running AI locally” means
In a local-inference workflow, the model processes the prompt on the device or server hosting it instead of sending the prompt to a cloud model. The UK Information Commissioner’s Office (ICO) describes hosting a model on the device that generated a query as one way to run inference locally. The model and its runtime still have to be installed or downloaded, and other parts of an application may still connect to outside services.
For a legal document, the text can leave the computer before inference if a cloud service performs file conversion, optical character recognition (OCR), search, retrieval, or another processing step. Keep the source file, any extracted text, prompt context, and inference on the controlled system if the goal is to avoid sending document content to a cloud service.
“Local” describes where processing happens; it is not a certification of privacy or security. It does not, on its own, establish confidentiality, attorney-client privilege, compliance with a particular law or engagement, or safe handling of client information. The ICO also notes that local processing can remain subject to data-protection law. Its guidance says it is under review following changes made by the Data (Use and Access) Act, so check the current page and applicable rules for your circumstances: ICO guidance on security and data minimisation in AI.
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Choose a local model runner
Ollama and LM Studio are two options with official documentation for running models locally. Their documentation is a starting point, not proof that a particular setup keeps every part of a legal-document workflow offline. Features, supported systems, models, and settings can change, so check the current documentation and configuration before deployment.
| Option | What the official documentation offers | What to verify for your use |
|---|---|---|
| Ollama | Documentation relevant to running models locally. | Current operating-system and model support, installation and update process, network behavior, logging, and whether your document-processing steps remain local. |
| LM Studio | Documentation relevant to running models locally. | Current operating-system and model support, installation and update process, network behavior, logging, and whether your document-processing steps remain local. |
There is no established winner for legal work here. Compare candidates using your actual requirements: document ingestion and OCR, context-window needs, access controls, firm deployment options, update practices, logs and telemetry, outbound connections, and output quality on a representative test set that contains no confidential material. Test the model on tasks similar to the intended use; do not assume that a general-purpose model can reliably interpret legal language.
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A controlled workflow for client documents
- Define the task and check permission. Decide whether the work is a limited task such as locating a clause or extracting dates, rather than asking the model to make a legal judgment. Classify the document and confirm that AI use is permitted by the engagement, applicable professional rules, any court or matter restrictions, and firm policy. If any of those requirements are unclear, resolve them before entering client information.
- Select an approved runner and model. Use software and a model source your organization can vet and support. Review current vendor documentation for supported systems, models, settings, updates, and privacy or network behavior. Record the runner and model versions used for the matter.
- Install through a controlled process. Obtain the runtime and model files from sources your organization trusts, and keep them updated under its normal security process. Third-party packages, data, models, and deployment platforms can introduce supply-chain vulnerabilities; Microsoft’s LLM security planning guidance identifies supplier vetting as part of managing these risks: Microsoft security planning for LLM applications.
- Verify the full processing path before use. Check how the runner and any document tools handle network connections, integrations, telemetry, and logs. Confirm that file parsing, OCR, retrieval, prompts, and inference do not route content to a cloud service. Do not rely on the word “local” or a product’s default settings as proof. The available documentation does not establish a tested offline configuration for either runner.
- Protect files, outputs, and logs. Limit access to the matter’s source documents and generated outputs. Avoid retaining unnecessary sensitive prompt content in logs; protect sensitive data at rest with encryption; and account for backups, retention, and deletion. Microsoft’s guidance also identifies least-privilege access, monitoring, outbound network controls, encryption, and supplier vetting as relevant security measures.
- Ask a narrow question and verify the result. Request a specific extraction or summary, preserve references to the source passages, and have a qualified person check every material statement against the original document and authoritative law. Treat the model’s response as work to review, not as a verified legal conclusion.
Security risks that remain when inference is local
Keeping prompts off a cloud inference service addresses one route of disclosure, but the local system still handles sensitive information. Consider the complete environment: the computer or server, user accounts and permissions, model runner, downloaded model files, document tools, logs, storage, backups, and any connected service. A weak device or overly broad access can expose data even if inference itself stays local.
- Unexpected network traffic: A local app or companion tool may have outbound connections or integrations. Determine what connects and why, and restrict outbound traffic where feasible under your organization’s security controls.
- Excessive retention: Prompts, extracted text, outputs, and diagnostic logs may contain client details. Set retention and deletion practices for each location where they could be stored, including backups.
- Uncontrolled access: Apply least-privilege access to source documents, outputs, and the system running the model. Matter data should not be available to users who do not need it.
- Untrusted software or model files: Vet suppliers and sources, track versions, and manage updates. Installing a model locally does not remove software supply-chain risk.
- Device and storage exposure: Protect sensitive data at rest and account for lost devices, shared machines, and backup copies. Encryption is one control, not a substitute for securing the rest of the workflow.
Hardware and legal judgment set practical limits
A local model must be small and computationally efficient enough for the device that runs it, as the ICO notes. No general minimum for RAM, GPU, storage, or model size is established here; requirements depend on the model, runner, document workflow, and files. Check the current requirements for the exact combination you intend to use rather than relying on a generic hardware threshold.
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Local execution also does not establish legal accuracy. No comparative accuracy testing on legal documents is available here, and running a model locally does not make its answer more reliable. Verify extracted facts, summaries, and legal analysis against the original material and authoritative sources.
Lawyers must also consider professional obligations beyond the technology choice. The American Bar Association’s Standing Committee on Ethics and Professional Responsibility, in Formal Opinion 512 issued July 29, 2024, identifies duties involving competent representation, protection of client information, communication with clients, supervision of employees and agents, candor, and reasonable fees. That is US ABA guidance, not a universal rule for every jurisdiction. Apply the rules and firm policies that govern the matter. Read the official ABA Formal Opinion 512.
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Before using real client material
- Confirm the proposed AI use is allowed for the matter and approved under firm policy.
- Use a controlled runner and model source, and record their versions.
- Verify where document conversion, OCR, retrieval, prompts, inference, logs, and outputs are processed or stored.
- Review network connections and integrations; apply outbound controls where feasible.
- Limit access, protect data at rest, and set retention and backup practices.
- Test the workflow and output quality with representative non-confidential material before introducing client documents.
- Require human review of every material result against the source and applicable law.
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