Self-Hosted PII Redaction for LLMs and AI Assistants
PII redaction for LLMs that runs inside your own network or cloud account. NeuralChainAI deploys a layer that finds names, account numbers, income and contact details in each prompt and masks them before the request reaches Claude, ChatGPT, Bedrock or a self-hosted model. Real values return in the answer, and every masked field is logged.
What PII Redaction for LLMs Does to Each Prompt
Six steps run on every request, and personal values stay inside your network at each one.
Intercept the prompt
Your chat interface, agent or app sends each prompt to the redaction service first.
Detect personal data
PII detection uses checksum-validated patterns for card and account numbers plus a named-entity model for names and places. Context words such as "account" raise confidence.
Mask or tokenize
Each finding becomes a placeholder such as [PERSON_1] or a reversible token, per the rule for that entity type.
Send the clean prompt
The model receives the masked prompt and reasons over the placeholders, so a reply can still say "call [PERSON_1] about account [ACCOUNT_1]".
Restore values in the response
Placeholders in the answer switch back to real values before the user sees it. The mapping table stays in your environment for as long as you set.
Log what was masked
Each request records the entity types found, the treatment, the user and the model, with the values left out.
What the Redaction Layer Catches in Prompts and Files
Built-in recognizers cover common PII data, and custom recognizers cover the identifiers only your organization uses.
Names
Names of members, customers and staff, found by a model that reads the sentence around each word.
Phone numbers, emails and addresses
Built-in recognizers for phones, emails and locations, plus one we add for street addresses.
Account and member IDs
Card numbers and IBANs checked by checksum and bank account numbers by pattern. Member, policy and case numbers use your own recognizers.
Income and financial details
Amounts next to words like "salary", "owe" or "balance", caught by context rules written for your data.
Government ID numbers
Social Security numbers, ITINs, passports and driver's licenses, plus national ID formats for many other countries.
Custom recognizers for your formats
Pattern and deny-list rules for IDs only you use, such as a member number prefix, added in code or per request.
Where the Redaction Layer Runs: Your Network, in Front of Any Model
Hosted AI redaction software runs detection on the vendor's servers. This layer runs as standalone containers in your data center or cloud account, so raw values stay inside your boundary.
We build the detection engine on Presidio, the open-source SDK many teams still search for as Microsoft Presidio. It started at Microsoft and is now a community-governed project under the MIT license. Presidio describes itself as a context aware, pluggable and customizable PII de-identification service for text and images.
In front of Claude, ChatGPT and Bedrock
Your apps, agents and private chat interface redact PII through the service before each API call, so Anthropic, OpenAI or a Bedrock endpoint in your AWS account receives placeholders in place of personal values.
Inside your LLM gateway
With a self-hosted LLM gateway, redaction runs on every routed request. LiteLLM's Presidio guardrail masks or blocks entities before the model call, and its output_parse_pii setting puts the original values back into the response.
In front of a self-hosted model
Prompts to a model you serve with vLLM stay on your servers, and redaction keeps logs, analytics and wider staff views masked.
Presidio's REST endpoints ship without built-in authentication, so the service sits behind your gateway or service mesh.
Masking, Tokenization or Removal: When Each One Fits
Each entity type gets its own treatment. PII masking, tokenization and removal balance readability against exposure differently:
Tokenize what the answer needs back. Remove the rest.
We set each treatment with your privacy lead, test it on your own prompts and keep the rules in version control.
Redaction Logs: What Was Masked, Who Sent It and How Long Logs Are Kept
Privacy and security teams can audit every request without seeing personal values.
What was masked
Entity types, counts, confidence scores and the treatment applied.
Who sent it
The user or service account, the calling app and the destination model.
How long logs are kept
Your policy sets retention. Logs and the token map that restores values sit in separate stores, each with its own access rules and deletion schedule.
Review and tuning
Staff review a sample of masked prompts and mark misses and false alarms. Presidio's documentation describes the trade-off between the two, so each mark becomes a test case or a rule change.
Raw Prompts, a Redaction Layer or a Self-Hosted Model
Three ways to bring AI to records that hold PII, for teams weighing AI redaction against a fully private model.
| Area | Raw prompts to a vendor | Redaction layer in front of a vendor | Fully self-hosted model |
|---|---|---|---|
| What leaves your network | Full prompts, personal values included | Prompts with placeholders and tokens | Prompts and answers stay on your servers |
| Prompt retention | The vendor's terms under your plan | Vendor terms cover masked text only | Your own policy |
| Models | The vendor's frontier models | The vendor's frontier models | Open-weight models you host |
| What the model sees | Real values | Placeholders in place of values | Real values |
| What your team runs | Vendor admin settings | Redaction containers, rules and a review queue | GPUs, model serving and the full stack |
| Best fit | Work free of personal data | Frontier-model answers on records that hold PII | Data that stays in-house end to end |
Many teams pair the last two and keep the most sensitive work on a self-hosted model, a setup our self-hosted AI stack covers end to end. Redaction suits any team whose prompts carry personal records, from nonprofits and membership associations holding member files to financial services teams handling account data.
What Shapes the Engagement
We scope each deployment in a strategy session, around these factors.
Data classes in scope
The personal data your prompts carry today and the fields your policy treats as sensitive.
Apps and channels
The chat tools, agents and pipelines that send prompts to a model.
Models and vendors
The models you call today and the vendor contracts already in place.
Custom identifiers
Member, account, policy or case number formats that need their own recognizers.
Languages
The languages your prompts arrive in, since each one needs its own detection model.
Review and retention
Who reviews flagged prompts and how long logs and token maps are kept.
Talk to Our Private AI Team About PII Redaction
Bring sample prompts, the models your teams use and the fields your privacy lead flags. We'll walk through where the redaction layer sits and how detection gets tested on your own records.
Book a Strategy Session →Frequently Asked Questions on PII Redaction
Automated detection misses some values. Presidio's own documentation says there is no guarantee it will find all sensitive information and advises adding other protections. We test detection on samples of your own records, tune rules from the misses and false alarms by entity type and keep a reviewer queue running after launch. For data that must stay in-house in every case, a self-hosted model is the stronger control.
Yes, for traffic that goes through their APIs. The service masks the prompt, forwards it to Anthropic or OpenAI and restores values in the answer. Prompts typed straight into the claude.ai or chatgpt.com apps go to the vendor directly, so teams that need redaction give staff a chat interface that routes through the layer.
Yes. Presidio is free to use under the MIT license, with packages for text, images and structured data. It ships as a library, so recognizers, thresholds, authentication and logging are the build work. Among data anonymization tools, Presidio's maintainers note that hosted services such as Azure AI Language and Amazon Comprehend often cover more entities, while Presidio is built to be customized.
It gives your counsel and auditors a documented PII protection control, showing which data classes leave your network and in what form, with a log entry per request. Your counsel decides which laws apply (GLBA or HIPAA, for example) and whether a setup meets them.
Presidio is free to use. Running costs are the servers hosting the detection containers in your cloud account or data center, billed by your provider. Build effort follows the factors under What Shapes the Engagement, which a strategy session sizes for your apps.
Related Private AI Solutions
Private AI for Nonprofits and Associations
Member services, grants and data work on private AI.
See the industry page →Financial Services AI
Custom AI and ML for teams that handle customer financial data.
See financial services AI →AI Strategy Session
A free 30-minute scoping call on your prompts, data classes and models.
Book a session →Ready to Mask PII Before Prompts Reach an AI Vendor?
A free 30-minute strategy session maps your prompts, data classes and models and sketches where redaction, a gateway or a self-hosted model fits. Our contact page reaches the same team.