I need a privacy vault that anonymizes data before sending it to public LLM APIs.

Building a local privacy vault/proxy that intercepts, anonymizes (masks/tokenizes) PII locally, and de-anonymizes LLM responses on the fly is the gold standard for safely leveraging public APIs (OpenAI, Anthropic, Gemini) without leaking sensitive data.

The architecture for this setup relies on an OpenAI-compatible local gateway paired with a robust PII detection and reversible masking engine.

Core Architecture

text``` [Your App / Client] │ (Sends standard OpenAI/Anthropic payload) ▼ [Local Privacy Proxy / Vault]

1. Intercepts request
2. Runs Local Analyzer (spaCy/Presidio/GLiNER)
3. Replaces PII with tokens (e.g., John Doe ➔ <<PERSON_1>>)
4. Stores mapping securely in local memory/encrypted cache │ (Sends sanitized payload) ▼ [Public LLM API: OpenAI / Anthropic / etc.] │ (Returns generated text with tokens) ▼ [Local Privacy Proxy / Vault]
5. Intercepts response
6. Restores original PII using local mapping table │ (Sends de-anonymized payload back) ▼ [Your App / Client]

Use code with caution.

### Top Open-Source Building Blocks & Tools

Instead of building from scratch, you can assemble a production-grade stack using these proven open-source components:

1. **The PII Detection & Anonymization Engine** 
	- **[Microsoft Presidio](https://github.com/microsoft/presidio):** The industry standard for context-aware, localized PII detection and anonymization/pseudonymization. It runs locally, supports custom entity recognizers, and integrates with spaCy or Hugging Face Transformers.
	- **LLM Guard (by Protect AI):** Includes robust input scanners specifically built to detect, redact, and anonymize PII before it reaches an LLM call.
	- **DataFog / GLiNER:** Lightweight, fast extraction models if you need cutting-edge zero-shot NER (Named Entity Recognition) locally.[](https://www.youtube.com/watch?v=RfxnD00xxwo&t=760) [[1]](https://www.youtube.com/watch?v=RfxnD00xxwo&t=760)[[2]](https://www.youtube.com/watch?v=zpCX9bTb6fE&t=57)[[3]](https://www.youtube.com/watch?v=_dAzcCk-3U4&t=43)[[4]](https://langfuse.com/blog/2024-06-monitoring-llm-security)[[5]](https://github.com/malteos/awesome-anonymization-for-llms)
2. **The Gateway / Proxy Layer** 
	- **Kiji Privacy Proxy:** An open-source local privacy proxy (supporting MacOS/Linux) designed to mask and revert PII locally using ONNX before forwarding requests to closed-source models.
	- **Custom Lightweight FastAPI / LiteLLM Proxy:** You can spin up a quick Python proxy that mimics the OpenAI `/v1/chat/completions` schema. It catches the incoming JSON, passes the `messages` array through Presidio or LLM Guard, swaps out entities while maintaining a conversation-stable dictionary map, forwards the clean payload to your target LLM, and reverses the replacement on the way back.
	- **Pre-built ecosystems:** Explore collaborative curated lists like [Awesome Anonymization for LLMs on GitHub](https://github.com/malteos/awesome-anonymization-for-llms) for plug-and-play dockerized proxies and community-built gateways.[](https://github.com/malteos/awesome-anonymization-for-llms) [[1]](https://github.com/malteos/awesome-anonymization-for-llms)[[2]](https://github.com/malteos/awesome-anonymization-for-llms)

### Key Implementation Gotchas to Keep in Mind

- **Context & Token Stability:** Ensure your mapping engine retains the *same* token for the same entity throughout a multi-turn conversation (e.g., `John` should always map to `<<PERSON_1>>` in turn 1 and turn 2, otherwise the LLM gets confused about pronoun references and context).[](https://github.com/malteos/awesome-anonymization-for-llms) [[1]](https://github.com/malteos/awesome-anonymization-for-llms)
- **State Management:** If your proxy is stateless, you cannot de-anonymize the response properly unless you use reversible pseudonymization backed by a short-lived, encrypted local cache (like an encrypted Redis or local in-memory LRU cache with AES-GCM).[](https://github.com/malteos/awesome-anonymization-for-llms) [[1]](https://github.com/malteos/awesome-anonymization-for-llms)
- **Regex vs. NLP Models:** Relying purely on Regex will miss contextual names or loosely structured data. Combining rule-based regex (for emails, credit cards, phones) with local transformer/NER models (for names, locations, organizations) gives the best balance of speed and safety.

To help tailor the exact implementation approach:

- Are you looking for a **ready-to-deploy open-source proxy binary/Docker container** , or do you want a **code snippet (Python/FastAPI)** to build a custom gateway?
- Which **LLM provider APIs** (OpenAI, Anthropic, etc.) are you targeting?

Yes. What you’re describing is essentially a privacy gateway/vault:

Your app → privacy vault → anonymize/tokenize → public LLM API → response → re-identify locally → your app

Good options

• Skyflow — probably the closest match if you want a commercial, production-oriented privacy vault specifically for GenAI. Its GenAI offering can de-identify sensitive data before inference and re-identify it afterward, while keeping the underlying data in the vault.
• Microsoft Presidio — my pick if you want to self-host. It detects PII and supports replacement, masking, hashing, encryption, and custom anonymization operators. It can run as Python packages or Docker services.
• Presidio + an LLM proxy — particularly attractive if you need one gateway in front of multiple providers. Presidio's examples include PII masking for LLM calls across providers such as Anthropic, Gemini, Bedrock, and Azure.

What I'd build

For a self-hosted "privacy vault," I'd use:

                 ┌─────────────────────┐
                 │   Your application  │
                 └──────────┬──────────┘
                            │
                     plaintext request
                            │
                 ┌──────────▼──────────┐
                 │   Privacy Gateway   │
                 │                     │
                 │ PII/secret detector │
                 │      ↓              │
                 │ Tokenizer / vault   │
                 │      ↓              │
                 │ Policy enforcement  │
                 └──────────┬──────────┘
                            │
                    sanitized request
                            │
             ┌──────────────▼──────────────┐
             │ Public LLM API              │
             │ OpenAI / Anthropic / Gemini │
             └──────────────┬──────────────┘
                            │
                     sanitized response
                            │
                 ┌──────────▼──────────┐
                 │ Re-identification   │
                 │ + policy validation │
                 └──────────┬──────────┘
                            │
                      original context

The important distinction is tokenization rather than simply redaction. For example:

John Smith emailed [email protected] about Acme Corp. becomes:

[PERSON_1] emailed [EMAIL_1] about [ORG_1]. The LLM can still reason about relationships and references. Your vault maintains:

PERSON_1 → John Smith
EMAIL_1  → [email protected]
ORG_1    → Acme Corp.

and only the vault can resolve those tokens.

One important caveat

Don't treat a PII detector as a guarantee of anonymity. Presidio itself explicitly warns that automated detection can miss sensitive information.

For a serious deployment I'd combine PII detection + secret detection + deterministic tokenization + encryption + allow/deny policies + outbound DLP checks. I'd also prevent the public LLM from ever receiving the token→real-value mapping.

If you tell me whether you want open-source/self-hosted, commercial/SaaS, or something you can deploy in your own VPC, I can narrow this to the best 3 choices and give you an architecture for it.

Yes. What you want is essentially an LLM privacy gateway / privacy vault:

Your app → PII detection + tokenization → public LLM API → tokenized response → re-identification → your app

Best fit: Skyflow

Skyflow is probably the closest off-the-shelf match to your description. Its LLM privacy tooling is specifically designed to detect sensitive information, replace it with tokens, send the de-identified content to external LLMs, and control when those tokens can be re-identified.

For example:

Original:
"Tell John Smith at [email protected] that his $14,500 invoice is overdue."

             ↓ privacy gateway

LLM sees:
"Tell [PERSON_7] at [EMAIL_3] that his [AMOUNT_12] invoice is overdue."

             ↓ public LLM

LLM response:
"Please contact [PERSON_7] regarding [AMOUNT_12]."

             ↓ vault

Your application sees:
"Please contact John Smith regarding $14,500."

Skyflow supports de-identification of text and other unstructured data, configurable token formats, and controlled re-identification. Skyflow Documentation It also provides APIs and gateway-style connections, so you can put it in front of downstream services rather than modifying every application individually.

If you want to build it yourself

A good lower-level building block is Microsoft Presidio. It's open source and provides PII detection, masking, redaction, and anonymization for text and images.

A self-hosted architecture could be:

                    ┌─────────────────────┐
                    │   Your application  │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │  Privacy Gateway    │
                    │                     │
                    │  1. Detect PII      │
                    │  2. Tokenize        │
                    │  3. Policy checks   │
                    └──────────┬──────────┘
                               │
                 "John" → USER_123
                 "john@..." → EMAIL_456
                               │
                               ▼
                    ┌─────────────────────┐
                    │ Public LLM API      │
                    │ OpenAI / Anthropic / │
                    │ Google / etc.       │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │ Re-identification   │
                    │ + output filtering  │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   Your application  │
                    └─────────────────────┘

Presidio can be installed as Python packages or Docker services, which makes this approach practical for a self-hosted gateway.

What I'd choose

• Need enterprise governance/compliance/audit trails: Skyflow.
• Need open source/self-hosted: Presidio + your own token vault.
• Need a lightweight internal proxy: Presidio behind an API gateway, with a small encrypted token-mapping database.
• Need the LLM to maintain context across multiple requests: use stable, per-session tokens rather than replacing names with generic [NAME] placeholders.

The important architectural point is that anonymization alone isn't enough. If you need the application to recover the original values, use reversible tokenization and keep the mapping outside the LLM provider. Skyflow explicitly supports this vault/token model.

If you tell me whether this is for a personal project, an internal company system, or a production/compliance-sensitive system, I can recommend a concrete architecture and stack (including the API gateway, token store, PII detector, and LLM proxy).

Original:
"Tell John Smith at [email protected] that his $14,500 invoice is overdue."

             ↓ privacy gateway

LLM sees:
"Tell [PERSON_7] at [EMAIL_3] that his [AMOUNT_12] invoice is overdue."

             ↓ public LLM

LLM response:
"Please contact [PERSON_7] regarding [AMOUNT_12]."

             ↓ vault

Your application sees:
"Please contact John Smith regarding $14,500."

Skyflow supports de-identification of text and other unstructured data, configurable token formats, and controlled re-identification. Skyflow Documentation It also provides APIs and gateway-style connections, so you can put it in front of downstream services rather than modifying every application individually.

If you want to build it yourself

A good lower-level building block is Microsoft Presidio. It's open source and provides PII detection, masking, redaction, and anonymization for text and images.

A self-hosted architecture could be:

                    ┌─────────────────────┐
                    │   Your application  │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │  Privacy Gateway    │
                    │                     │
                    │  1. Detect PII      │
                    │  2. Tokenize        │
                    │  3. Policy checks   │
                    └──────────┬──────────┘
                               │
                 "John" → USER_123
                 "john@..." → EMAIL_456
                               │
                               ▼
                    ┌─────────────────────┐
                    │ Public LLM API      │
                    │ OpenAI / Anthropic / │
                    │ Google / etc.       │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │ Re-identification   │
                    │ + output filtering  │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   Your application  │
                    └─────────────────────┘

Presidio can be installed as Python packages or Docker services, which makes this approach practical for a self-hosted gateway.

What I'd choose

A privacy vault and proxy architecture intercepts user prompts, detects and masks Personally Identifiable Information (PII) using a secure local vault, forwards an anonymized payload to public LLM APIs, and deanonymizes the returned response before showing it to the end user.

The implementation relies on an open-source interceptor proxy combined with an entity-recognition and pseudonymization engine.

Core Architecture Components

• PII Detection Engine : Scans incoming text using regular expressions for structured data (e.g., SSNs, credit cards, phone numbers) and Named Entity Recognition (NER) models for unstructured context like names, locations, and organizations.
• Local Privacy Vault : Stores a temporary, encrypted mapping (using AES-256-GCM) of original entities to surrogate/pseudonymized tokens (e.g., mapping "Sarah Williams" to "Pamela Nelson").
• LLM Proxy Middleware : Forwards the tokenized, sanitised prompt to public endpoints like OpenAI, Anthropic, or custom APIs.
• Reversal/Deanonymization Layer : Intercepts the LLM response, queries the local vault to swap surrogate tokens back to original values, and delivers the finalized context.

Open-Source Tools & Frameworks

Implementation Workflow

1. Capture : The application route intercepts the user's prompt before it exits your secure boundary.
2. Scan & Replace : Run the text through Microsoft Presidio to swap sensitive strings with realistic surrogate data (pseudonymization maintains grammatical coherence for the LLM).
3. Vault Storage : Save the key-value map inside a local encrypted database or memory cache with a short Time-To-Live (TTL).
4. Dispatch : Send the clean payload to the public LLM API.
5. Restore : Receive the generated text, substitute the surrogate tokens back with original entities via your local vault, and output the result.

If you'd like to proceed, tell me:

• Are you looking to deploy this as a Python middleware library or a standalone proxy server (like Docker)?
• Which LLM provider APIs (OpenAI, Anthropic, etc.) are you integrating with?

A privacy vault or proxy for LLM APIs acts as a local interceptor: it scans outgoing prompts for Personally Identifiable Information (PII) or sensitive data, replaces/vaults it with safe tokens or pseudonyms locally, sends the sanitized payload to public LLM providers (like OpenAI or Anthropic), and then unmasks/restores the original values when the response comes back.

The primary open-source architectures and tools capable of handling this pattern vary depending on your operational setup:

1. Dedicated Open-Source Privacy Proxies

• Philter AI Proxy: A self-hosted, open-source proxy designed to run on your local infrastructure as the last hop before traffic leaves your network. It handles text redaction and audit trails, sitting right in front of external APIs without acting as a heavy full-featured AI gateway.
• CloakPipe: A high-performance privacy middleware and proxy that sits between your application and any LLM API. It detects PII, masks it, handles the upstream proxy call, and uses an encrypted vault (AES-256-GCM) to restore original values on return.
• LLM-Shield: A simpler Docker-compose-ready privacy proxy focused on masking PII, with options to dynamically route highly sensitive queries to a local LLM instead of a public API.

2. The Enterprise Standard Engine: Microsoft Presidio

If you want to build a custom vault or integrate anonymization into an existing gateway (like LiteLLM or Portkey), Microsoft Presidio is the underlying open-source engine of choice.

• How it works: Presidio uses modular analyzers (spaCy or Hugging Face transformers) to identify entities like names, phone numbers, credit cards, and locations.
• Anonymizer/Vault: Its anonymizer module can pseudonymize, hash, or reversibly encrypt/vault tokens so that the downstream public LLM only ever interacts with non-attributable placeholders.

To help point you to the best implementation, tell me:

• Are you looking for a plug-and-play proxy container (like Docker) or a Python/Node.js library to embed in your app code?
• Which public LLM providers (OpenAI, Anthropic, Bedrock) are you routing through?

If you need a privacy vault / gateway that strips or tokenizes sensitive data before sending prompts to public LLM APIs, look for an LLM privacy gateway (also called a PII redaction gateway, AI firewall, or de-identification proxy). These sit between your application and providers like OpenAI, Anthropic, or Google and replace sensitive values with placeholders before the request leaves your environment.

Here are some options:

A typical architecture looks like:

Your App
   |
   v
Privacy Vault / LLM Gateway
   |
   |  "John Smith" → "<PERSON_1>"
   |  "[email protected]" → "<EMAIL_1>"
   |
   v
Public LLM API
   |
   v
Gateway restores values (if allowed)
   |
   v
Your App

For a production deployment, I’d look for these capabilities:

• Local/self-hosted processing so raw data never reaches the vendor
• Reversible tokenization (vaulted mappings) instead of simple redaction
• PII + secrets detection (names, emails, SSNs, API keys, credentials, customer IDs)
• Structured JSON support in addition to plain text
• Tenant policies (for example, block sending payment data but allow names)
• Audit logs without storing prompt contents
• Provider-agnostic routing so you can switch between OpenAI, Anthropic, Azure OpenAI, etc.

A strong open-source starting point is Presidio + an API gateway layer if you want full control. If you want a ready-made proxy with minimal engineering, an LLM gateway product is usually faster to deploy.

If you tell me your environment (AWS/Azure/GCP/on-prem, language stack, compliance needs like HIPAA/GDPR/SOC 2, and whether you need reversible anonymization), I can narrow this down to a few best-fit choices.

To safely use public LLM APIs (like OpenAI, Anthropic, or Gemini) without leaking sensitive personal or corporate data, you need an LLM Privacy Proxy with an in-memory/encrypted vault . This architecture intercepts your prompt, detects and masks Personally Identifiable Information (PII), forwards the sanitized text to the LLM, and then unmasks/restores the original data in the returned response if needed.

Several open-source projects, tools, and architectural stacks handle this exact workflow:

• CloakPipe — A high-performance privacy proxy that sits in your application path, detects PII, swaps it for safe tokens, proxies the request to any LLM API, and unmasks the response using an encrypted vault (AES-256-GCM).
• PII Shield — An intelligent anonymization layer built on FastAPI and Microsoft Presidio that supports reversible tokenization, hashing, or encryption strategies before hitting external models.
• Philterd (PII Guardrails for AI) — A self-hosted, open-source proxy running inside your VPC that acts as a drop-in replacement for OpenAI, Claude, and Ollama to scrub PII locally before data ever leaves your perimeter.
• Open Privacy Vault (OPV) — A security-first open privacy vault featuring pluggable encryption engines and tokenization strategies that never store plaintext PII.

How the Architecture Works

1. Interception: Your app sends the prompt to the local proxy instead of directly to OpenAI/Anthropic.
2. Detection & Masking: An NLP engine (such as Microsoft Presidio or spaCy) scans for names, emails, financial records, or custom regex patterns.
3. Vault Storage: The real data is mapped to a secure, ephemeral, or encrypted token and stored in a local vault.
4. LLM Execution: The sanitized prompt (containing placeholder tokens like <PERSON_1> ) goes to the public LLM.
5. Reversal: The response comes back, and the proxy swaps the placeholders back to the original values so the end user sees a coherent answer.

To help narrow down the right setup, tell me:

• Are you looking for a ready-to-deploy open-source proxy (like Philterd or CloakPipe) or do you want to build a custom pipeline using Python and Microsoft Presidio?
• Do you need the anonymization to be fully reversible (deanonymized in the final response) or one-way?

Yes. What you want is essentially a privacy gateway/vault sitting between your application and public LLM providers:

Your app → Privacy Vault → anonymize/tokenize → public LLM → de-tokenize → your app

Best options

• Skyflow — probably the closest match to your description. Its privacy vault can detect and de-identify PII/PHI/PCI, replace sensitive values with tokens, and later re-identify them under controlled access. It explicitly supports keeping PII out of external LLMs.
• Microsoft Presidio — best if you want to self-host the privacy layer rather than send the sensitive data to another SaaS. It's an open-source toolkit for detecting and anonymizing PII in text, images, and structured data, with customizable recognizers and deployment via Python, Docker, or Kubernetes.

Architecture I'd recommend

For a serious implementation, don't merely redact:

"John Smith's account 483921 has a problem" into:

"REDACTED's account REDACTED has a problem" Instead, use consistent reversible tokens:

"Customer <PERSON_7>'s account <ACCOUNT_42> has a problem" The vault maintains something like:

<PERSON_7>  ↔  John Smith
<ACCOUNT_42> ↔  483921

The public LLM sees only the tokenized prompt. Its response can then pass back through the vault, where authorized tokens are restored.

I'd also put the vault behind an API gateway and enforce:

• PII/PHI/PCI/credential detection
• deterministic tokens within a conversation
• encryption of the token mapping
• tenant/user isolation
• audit logs
• configurable "never send" patterns
• output scanning before de-tokenization
• protection against prompt injection attempting to expose the underlying values
• fail-closed behavior when sensitive data cannot confidently be classified

One important caveat: automated anonymization isn't perfect. Presidio itself warns that automated detection can miss sensitive information, so it should be supplemented with rules, secret detection, allow/deny lists, and testing against your actual data.

If you're building this yourself, I'd choose Presidio + a small token vault + an LLM proxy. If you want a managed enterprise product, I'd start with Skyflow.

"Customer <PERSON_7>'s account <ACCOUNT_42> has a problem" The vault maintains something like:

<PERSON_7>  ↔  John Smith
<ACCOUNT_42> ↔  483921

The public LLM sees only the tokenized prompt. Its response can then pass back through the vault, where authorized tokens are restored.

I'd also put the vault behind an API gateway and enforce:

• PII/PHI/PCI/credential detection
• deterministic tokens within a conversation
• encryption of the token mapping
• tenant/user isolation
• audit logs
• configurable "never send" patterns
• output scanning before de-tokenization
• protection against prompt injection attempting to expose the underlying values
• fail-closed behavior when sensitive data cannot confidently be classified

One important caveat: automated anonymization isn't perfect. Presidio itself warns that automated detection can miss sensitive information, so it should be supplemented with rules, secret detection, allow/deny lists, and testing against your actual data.

Yes. What you want is usually called a reversible pseudonymization/privacy proxy: your application sends data to the vault, the vault replaces sensitive values with consistent synthetic tokens, the public LLM sees only those tokens, and the vault restores the originals in the response.

A good architecture is:

Your app
   │
   ▼
┌──────────────────────────────┐
│       Privacy Vault          │
│                              │
│  1. Detect PII/secrets       │
│  2. Generate stable tokens   │
│  3. Encrypted mapping store  │
│  4. Audit / policy controls  │
└──────────────┬───────────────┘
               │
       sanitized prompt
               │
               ▼
       Public LLM API
   OpenAI / Anthropic / Gemini
               │
          response
               ▼
       Privacy Vault
               │
       detokenize response
               ▼
            Your app

Options I'd look at

• github.com — particularly close to your description. It sits between an agent and the model provider, uses stable tokens, and keeps the token→original mapping in an encrypted per-conversation vault.
• philterd.github.io — self-hostable proxy supporting OpenAI, Anthropic, Gemini, Bedrock and others. It focuses on PII/PHI redaction and can handle streaming.
• docs.anonamoose.net — drop-in proxy that redacts/tokenizes requests and rehydrates responses, including streaming responses.
• github.com — interesting if you want something local and lightweight; its vault stays on the machine and it supports streamed responses.
• github.com — excellent building block if you want to build the vault yourself rather than adopt a complete proxy. It provides PII detection/anonymization across text, images and structured data.

One important distinction

I would not make simple redaction your default:

John Smith owes $500 → [PERSON] owes $500 A better vault does:

John Smith owes $500 → Marcus Hale owes $500 and remembers:

Marcus Hale  →  John Smith

The synthetic identity preserves relationships and makes the prompt much more useful to the LLM. Recent research specifically finds surrogate substitution can preserve substantially more semantic utility than ordinary placeholder redaction.

For a serious implementation, I'd also make the entire conversation history use surrogates—not merely sanitize the latest message. Otherwise real PII can remain in the API-side conversation history and get retransmitted later.

If you tell me whether this is for a personal/local setup, an internal company service, or a production SaaS, I can recommend the best architecture and give you a concrete stack (e.g. Docker + Presidio + encrypted vault + OpenAI/Anthropic-compatible proxy).

To anonymize and protect sensitive data before it reaches public LLM endpoints (like OpenAI, Anthropic, or Google), you need a sandwich-pattern privacy proxy or gateway . This setup intercepts the prompt, detects and masks Personally Identifiable Information (PII) / sensitive tokens, sends the sanitized payload to the external LLM, and then reverses the process (de-anonymizes) on the incoming response stream before presenting it back to your application.

The leading tools, architectural approaches, and open-source stacks available to build or deploy a privacy vault for LLMs include:

1. Ready-to-Use Open-Source & Enterprise Stacks

• Microsoft Presidio:
  • What it is: An open-source, highly extensible PII detection and anonymization framework. It uses a combination of Named Entity Recognition (NER via spaCy), regex patterns, and checksums to identify names, locations, phone numbers, financial data, and custom keys.
  • How it works as a vault: It acts as the core engine to anonymize (replace with placeholders or pseudonymized values) on outbound traffic and deanonymize on inbound responses.
• PII Shield (Reference Architecture / FastAPI + Presidio):
  • What it is: Microsoft’s pattern/service implementation combining a stateless FastAPI service with a Presidio engine, designed explicitly to integrate with API Management gateways (like Azure APIM or custom reverse proxies) to intercept, scrub, route, and unmask LLM calls.
• LiteLLM / Portkey / Langfuse Gateway Layers:
  • What they are: Standard LLM gateway proxies used for load balancing and cost tracking. Many teams write custom middleware or use integrated hooks in these gateways to pass prompt text through an anonymization microservice (like Presidio or custom regex/NER mapping) right before dispatching the payload to the external LLM vendor.

2. The Core Architecture: How a "Privacy Vault" Works

A robust privacy vault doesn't just "erase" data (which often breaks the LLM's grammatical context or conversational logic); it pseudonymizes or tokenizes it.

- Your application sends the raw prompt to your local proxy/vault instead of the public LLM.
- The engine scans the text using NER models and custom rules.
- Sensitive entities (e.g., `Jane Doe` → `<PERSON_1>`, `Acme Corp` → `<ORG_1>` ) are swapped using a deterministic or session-locked lookup table stored securely inside your local boundary.[](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/introducing-pii-shield-a-privacy-proxy-for-every-llm-call/4514726) [[1]](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/introducing-pii-shield-a-privacy-proxy-for-every-llm-call/4514726)[[2]](https://www.youtube.com/watch?v=RfxnD00xxwo&t=760)[[3]](https://langfuse.com/resources/engineering/pii-masking-llm-applications)[[4]](https://www.youtube.com/watch?v=zpCX9bTb6fE&t=57)

- The public LLM receives clean, contextually sound text stripped of real-world secrets.[](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/introducing-pii-shield-a-privacy-proxy-for-every-llm-call/4514726) [[1]](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/introducing-pii-shield-a-privacy-proxy-for-every-llm-call/4514726)

- The LLM generates a response referencing `<PERSON_1>`.
- The privacy proxy intercepts the response, matches the session identifiers, swaps the placeholders back to `Jane Doe` , and streams the clean output to your user.[](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/introducing-pii-shield-a-privacy-proxy-for-every-llm-call/4514726) [[1]](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/introducing-pii-shield-a-privacy-proxy-for-every-llm-call/4514726)[[2]](https://www.reddit.com/r/LLMDevs/comments/1sm61t9/i_built_an_opensource_token_proxy_that/)

3. Key Design Choices to Consider

• Stateful vs. Stateless Mapping: If you need the LLM to understand relationships across a long conversation, your proxy must maintain a temporary, encrypted in-memory session store (Redis) so that Jane Doe always maps to the same placeholder (<PERSON_1> ) throughout that specific chat thread.
• Streaming Support (SSE): If you use token streaming (Server-Sent Events), your proxy needs a small tail-buffer (holding the last ~80 characters) to ensure split tokens or split placeholder names across chunk boundaries don't break the de-anonymization parser.
• Preserving Metadata: Raw masking (like turning all IP addresses into 0.0.0.0 ) can destroy logic if you are asking the LLM to debug server logs or analyze security events. Advanced implementations use context-aware or subnet-preserving pseudonymization.

If you'd like to narrow this down, let me know:

• Are you looking to deploy a self-hosted open-source microservice (like Python/FastAPI + Presidio), or an API Gateway middleware?
• Do you need support for real-time token streaming?