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To remove client names from a draft before a local AI model sees it, run a PII detection tool over a copy of the file, review each name it flags, and replace or delete every client reference with a consistent placeholder. Presidio, a PII detection and anonymization project, is the most fully documented option for this workflow. Automated detection speeds up the job, but it does not guarantee that every client name is caught, so a human review pass is part of the method.
Decide what counts as identifying before you start
A client name is rarely the only identifier in a working draft. Before you run any tool, list the categories you need to handle:
- Legal names, trading names, and abbreviations of the client organization (for example, “Harlow Logistics” and “HL”).
- Personal names of contacts, approvers, and staff at the client.
- Internal project codenames that map directly to a client.
- Email addresses, domains, phone numbers, and street addresses.
- Invoice, contract, or order numbers that can be looked up.
Decide in advance which of these the AI task actually needs. If the model only has to tighten the prose of a case study, the client’s identity is probably irrelevant. If it has to summarize who requested what, you need stable stand-ins so the roles stay readable.
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Set up the detection and transformation pipeline
Presidio separates two jobs. The Analyzer identifies potential personal information in text and returns the detected spans with their entity types. The Anonymizer then applies an operation to those spans, such as redacting, replacing, masking, hashing, or encrypting them. Keeping these stages apart means you can review what was found before anything changes.
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Install Presidio
- Confirm that your Python version is 3.10, 3.11, 3.12, or 3.13. Presidio’s installation documentation lists these as supported.
- Install the two core packages with pip:
pip install presidio-analyzer presidio-anonymizer. - Install an NLP engine for the Analyzer, following the setup steps on the Presidio installation page. Name-based detection depends on this engine; without it, you lose much of the person-name coverage.
- If you prefer containers, use the Docker route described in the same documentation. For production use, the documentation advises pinning an explicit release tag rather than a floating one.
Presidio is described as moving toward community ownership, so check the current project documentation and package registry for the exact package names and commands before you publish or copy them into a team guide.
Add your client list as custom recognition
Built-in recognizers know what a person name or email address looks like. They do not know that “Harlow Logistics” is your client. Presidio supports predefined and custom recognizers, including deny lists, which let you add the exact client names, abbreviations, and codenames you maintain. Keep this list in a controlled file outside the draft, and update it whenever a new client or alias appears.
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Run the workflow on a copy of the draft
- Save a working copy of the draft and leave the original untouched in its usual secure location.
- Export the copy to plain text if possible. Formatting, comments, and tracked changes can hide text from simple scanners.
- Run the Analyzer over the text and record each detected span, its entity type, and its position.
- Review the list. Remove false positives, such as a product name that resembles a person’s name, and add any client names, aliases, or project terms the tool missed.
- Choose an operator for each entity type (see the table below) and run the Anonymizer on the reviewed results.
- Read the processed draft from start to finish before sending it to any model.
Illustrative example, with invented names: the sentence “Harlow Logistics asked Dana Reyes to approve the rollout” becomes “[CLIENT_1] asked [CONTACT_1] to approve the rollout” when stable replacement is used. Every later mention of Harlow Logistics should map to the same [CLIENT_1], which is what keeps the draft coherent.
Choose the right operator for each entity
| Approach | What it does | When it fits | Trade-off |
|---|---|---|---|
| Redact | Removes the detected span entirely | The name has no role in the AI task | Loses who did what, so later sentences can read oddly |
| Replace | Substitutes a placeholder such as [CLIENT_1] |
The draft needs roles and references to stay understandable | Every repeated mention must map to the same placeholder, which requires a consistent mapping table |
| Mask | Replaces some or all characters with a symbol | Partial concealment suits an internal review copy | Fragments can still reveal a recognizable name |
| Hash or encrypt | Transforms the value into a token; encryption can be reversed with a key | A controlled workflow needs repeatable tokens or later restoration | Restoration needs a protected key or mapping; hashing does not remove all re-identification risk |
For most client drafts, replacement with stable placeholders is the safest default. Use encryption only when someone genuinely needs to restore the original names, and store the key separately from the processed text.
Rank #3
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
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Catch what the tool does not flag
Automated detection finds the patterns it was built or configured to find. It often misses the following, so check for them by hand:
- Misspellings, possessives, and spacing variants of the client name (for example, “Harlow’s” or “Harlow-Logistics”).
- Indirect identifiers, such as a rare product line, a city and industry combination, or a unique project date that points to one client.
- Quoted material, signature blocks, email headers pasted into the body, and footers.
- File metadata and comments. Word documents, for example, can carry author fields, revision history, and comment threads that are not visible in the main text.
Presidio’s documentation does not promise protection against re-identification from context, so the final read is your responsibility.
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Check the local AI application before pasting anything
“Local” describes where a model runs, not what every surrounding application does. Each product has its own privacy statements, so check the one you actually use.
Ollama
Ollama’s privacy policy, marked last updated March 2026, says the software runs on the local device and that prompts and responses are not used to train models. The same policy describes the collection of device and usage information. Read both parts: the model inference stays local under the policy’s wording, but the application may still send some device and usage data. Review the settings and network behavior on your machine if that matters for your client agreements.
Microsoft Foundry Local
Microsoft’s Windows AI FAQ states that input data for Foundry Local is not sent to Microsoft servers. That statement is specific to Foundry Local as Microsoft documents it. It does not extend to other Windows AI features, third-party runtimes, or plug-ins you install alongside it.
Limits of this approach
- No detection-accuracy or recall figure for Presidio applies to your documents. Its documentation describes the recognizer types but does not publish a general accuracy number for client names. Measure the tool on a sample of your own drafts before relying on it.
- The method reduces exposure. It does not make a draft anonymous in the legal sense, and it does not replace your firm’s client-confidentiality terms or data-handling rules.
- Installation commands, package names, and supported Python versions can change, so verify them against the current Presidio documentation at the time you follow them.
The workable pattern is clear: copy the draft, detect and review, replace with consistent placeholders, check indirect identifiers and metadata, and confirm the privacy terms of the local application you use.
Quick Recap
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