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

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?

Yes. What you’re describing is essentially a privacy proxy/vault in front of LLM APIs:

Your app → detect sensitive data → tokenize/anonymize → public LLM → restore tokens → your app

Best fit: Skyflow

Skyflow is probably the closest off-the-shelf match. Its privacy vault can detect PII/PHI/PCI in unstructured text, replace it with tokens, keep the underlying sensitive values in the vault, and later re-identify them under access controls. It explicitly supports protecting data sent to external LLMs.

For example:

Before:
"John Smith at [email protected] needs his $4,200 invoice corrected."

LLM sees:
"[PERSON_7] at [EMAIL_3] needs his [AMOUNT_12] invoice corrected."

Your application receives:
"[PERSON_7] at [EMAIL_3] needs his [AMOUNT_12] invoice corrected."

Vault restores:
"John Smith at [email protected] needs his $4,200 invoice corrected."

Skyflow supports configurable tokenization/de-identification and can operate on text, PDFs, documents, images, and audio. Skyflow Docs It also offers different deployment models, including VPC and BYOC options, which can matter if the vault itself must remain under your security boundary.

Lower-cost / self-hosted approach: Microsoft Presidio

If you don't need a managed vault, Microsoft Presidio is a strong building block. It's open source and provides PII detection plus anonymization/de-anonymization, with operators such as replacement, masking, redaction, hashing, and encryption.

You could deploy it entirely inside your infrastructure:

             ┌──────────────────────┐
             │   Your application   │
             └──────────┬───────────┘
                        │
                  sensitive prompt
                        │
             ┌──────────▼───────────┐
             │ Privacy gateway      │
             │                      │
             │ Presidio / custom    │
             │ PII detection        │
             │ tokenization         │
             │ policy enforcement   │
             └──────────┬───────────┘
                        │
                  anonymized prompt
                        │
             ┌──────────▼───────────┐
             │ Public LLM API       │
             │ OpenAI / Anthropic / │
             │ Gemini / etc.        │
             └──────────┬───────────┘
                        │
                  anonymized answer
                        │
             ┌──────────▼───────────┐
             │ Re-identification   │
             │ + policy checks     │
             └──────────┬───────────┘
                        │
                  safe response

Presidio can be installed as Python packages or containerized services, making it practical for a self-hosted gateway.

One important design choice

I would not simply redact everything. For LLM workloads, deterministic tokens are usually better:

John Smith → PERSON_001 Acme Corporation → COMPANY_001 [email protected] → EMAIL_001 That lets the model reason about relationships consistently across a conversation while never receiving the actual identity.

I'd also make the gateway handle more than PII:

• PII: names, emails, phones, addresses, IDs
• Financial: account/card numbers, transaction details
• Healthcare: patient identifiers and clinical identifiers
• Credentials: API keys, passwords, access tokens
• Company secrets: customer names, proprietary project names, internal URLs
• Prompt/output logging: ensure the raw prompt never gets logged by the gateway or downstream LLM provider
• Re-identification: allow only the authorized application/service to detokenize

My recommendation: if this is an enterprise product and you want a real "vault," start by evaluating Skyflow. If you're building infrastructure yourself and want maximum control/cost efficiency, use Presidio + a small policy/token vault + an LLM proxy.

If you tell me whether you're looking for self-hosted, SaaS, or something you can deploy in AWS/Azure/GCP, I can narrow this down to 3–5 concrete products and compare their architecture, pricing, deployment, and LLM integrations.

To safely leverage public LLM APIs (like OpenAI, Anthropic, or Gemini) without leaking Personally Identifiable Information (PII) or sensitive enterprise data, you need a PII Proxy / Masking Architecture.

The standard design pattern intercepts your payload locally, strips or tokenizes sensitive data into placeholders, sends the sanitized payload to the public LLM, and then deanonymizes/restores the response on its way back to your application.

Top Open-Source & Enterprise Options for LLM Data Anonymization

1. Microsoft Presidio (As an LLM Gateway/Guardrail)
   • How it works: Presidio is an open-source analyzer and anonymizer framework. It detects PII (names, SSNs, financial data, locations) using NLP and regex, then replaces them with synthetic placeholders.
   • Integration: It integrates natively as a guardrail with routing tools like LiteLLM Proxy PII Masking or via custom middleware.
   • Best for: Self-hosted, highly customizable PII detection running entirely on your own infrastructure.
2. LLM-Guard (by Protect AI)
   • How it works: A comprehensive open-source security toolkit designed specifically for LLMs. It includes dedicated scanners to sanitize inputs (detecting and anonymizing PII/secrets) and evaluate outputs before they reach the user.
   • Best for: Production pipelines requiring robust input filtering and prompt injection defense alongside PII redaction.
3. Skyflow or Piiano Vault (Data Privacy Vaults)
   • How it works: Rather than just acting as a pass-through text scrubber, these are dedicated zero-trust data privacy vaults. You tokenize or store core sensitive database elements inside the vault, passing only safe reference tokens to your applications and AI workflows.
   • Best for: Enterprise setups where sensitive data shouldn't even reside in application logs or standard databases let alone prompts.

How the Architecture Works (The PII Proxy Pattern)

text``` [User / App] │ (Sends prompt with "Call John Doe at 555-0199") ▼ [Privacy Proxy / Vault Layer] │ (Detects PII & replaces with tokens: "Call <PERSON_1> at <PHONE_1>") ▼ [Public LLM API] (Processes only the anonymized text) │ (Returns response referencing "<PERSON_1>") ▼ [Privacy Proxy / Vault Layer] │ (Swaps tokens back to "John Doe") ▼ [User / App] (Receives complete, contextual response)

---

<FollowUp>
To help you zero in on the right implementation, tell me:
* Are you looking for a **self-hosted open-source stack** (like Microsoft Presidio + LiteLLM) or a **managed enterprise vault**?
* What **programming language or framework** is your backend built on?
</FollowUp>

Use code with caution.

Yes. What you’re describing is essentially a privacy gateway/vault in front of public LLM APIs:

Your app → privacy vault → anonymized/tokenized prompt → OpenAI/Anthropic/Gemini/etc. → tokenized response → vault → re-identified response

Best fits

My recommendation

If you want a ready-made privacy vault, I'd investigate Skyflow first. Its design is unusually close to your requirement: sensitive values are detected and replaced with deterministic tokens before being sent to an external LLM, while the original values remain in the vault and can be restored afterward under access controls.

If you need self-hosted/on-prem and don't want the vault provider itself seeing the plaintext, I'd use Presidio as the detection/anonymization layer and build a small gateway around it. Presidio can run as containerized services, and its anonymizer supports reversible encryption as well as replacement/redaction.

One important caveat: PII detection isn't perfect. Presidio itself warns that it cannot guarantee detection of all sensitive information, so a production gateway should combine NER/PII detection with regexes, custom dictionaries, allow/deny rules, and ideally an output scanner.

If you tell me whether you want (1) SaaS, (2) self-hosted, or (3) open-source, I can compare the best 5–10 options specifically on anonymization quality, reversibility, deployment, pricing, and whether the vault provider ever sees plaintext.

To route data safely through public LLM APIs (like OpenAI, Anthropic, or Groq) without leaking sensitive customer information or PII, you need a Privacy-First LLM Gateway or Local Proxy with an In-Memory/Encrypted Vault.

The architecture for this pattern follows a specific flow: Intercept → Detect PII → Tokenize/Mask → Proxy to LLM → De-anonymize Response.

Top Open-Source & Enterprise Solutions

• Kiji Privacy Proxy : An open-source local gateway by Dataiku that intercepts traffic locally (using a quantized DistilBERT model via ONNX runtime) to mask 16+ categories of PII before requests leave your machine or network, restoring the original values on the way back.
• CloakPipe : A lightweight privacy middleware and proxy for LLM/RAG pipelines that detects PII, swaps it for safe tokens, proxies the request, and uses an AES-256-GCM encrypted vault to restore original values in the response.
• Open Privacy Vault (OPV) : An open-source PII-as-a-service toolkit featuring database adapters and pluggable encryption engines that never stores plaintext and handles tokenization/redaction policies natively.
• Skyflow Data Privacy Vault : An enterprise-grade data privacy vault that isolates and governs sensitive data, offering tokenization and integration options for external LLMs via zero-data-leakage architecture.

How to Build a Lightweight Custom Version (Python + Presidio)

If you prefer rolling your own local proxy middleware, you can combine Microsoft's open-source Presidio analyzer with an in-memory dictionary or encrypted vault:

1. Detect & Mask : Use presidio-analyzer and presidio-anonymizer to scan input text, extract entities (names, emails, financial records), and replace them with unique placeholders (<PERSON_1>, <EMAIL_1>).
2. Store Mapping : Save the mapping of Placeholder $\rightarrow$ Original Value in a secure, ephemeral in-memory vault or an AES-encrypted local cache for the duration of the session.
3. Forward : Send the sanitized string to your public LLM API endpoint.
4. Restore : Take the LLM's response, look up the placeholders in your local vault, and re-inject the original data before returning it to your application interface.

If you want to dive deeper, let me know:

• Are you looking for a ready-to-deploy self-hosted proxy (like Docker) or a Python library to integrate into an existing app?
• What specific types of data are you most concerned about protecting (e.g., PHI/medical, financial/PCI, or general PII)?

If you need a privacy vault or proxy that intercepts prompts, strips out Personally Identifiable Information (PII) and sensitive data, and then forwards the anonymized payload to public LLM APIs (like OpenAI, Anthropic, or Cohere), there are several robust open-source tools and enterprise platforms designed specifically for this.

Here are the top options broken down by how you want to deploy and use them:

Open-Source & Self-Hosted Gateways

• Presidio (by Microsoft) : An open-source PII anonymization and de-anonymization SDK. You can run it locally or as a service to inspect text, detect entities like names, phone numbers, and keys, and mask them before they ever leave your infrastructure.
• LiteLLM : A popular proxy that lets you call 100+ LLMs using a unified OpenAI format. It includes built-in PII masking integration (often hooking into Presidio or Regular Expressions) to redact secrets and private data natively at the proxy level.
• PrivateGPT / Local AI Stack : If your goal is zero data leakage, running a localized orchestrator allows you to handle sensitive queries locally via open models, while routing non-sensitive tasks outward.

Enterprise & Managed Privacy Vaults

• Skyflow : Offers a data privacy vault API that isolates and tokenizes sensitive customer data. You can substitute real data with tokens inside your secure perimeter and let the LLM process the tokenized prompt safely.
• Proton Sentinel / Enterprise LLM Proxies : Specialized commercial AI gateways (such as those offered by vendors like Lasso Security, Lakera, or Concord) sit between your application and public LLM endpoints to block and scrub prompt injections, secrets, and PII in real time.
• Confidential Computing Enclaves : Platforms provided by major cloud providers utilizing hardware-based trusted execution environments (TEEs) to ensure data is encrypted even while in memory during processing by third-party APIs.

If you can tell me:

• Whether you prefer an open-source self-hosted tool or a managed commercial service
• Your primary programming language or stack (Python, Node.js, etc.)

I can give you a quick configuration example or recommend the best integration path for your setup.

If you mean a privacy vault/proxy that sits between your applications and public LLM APIs, you’re looking for a PII de-identification gateway (sometimes called an AI privacy gateway, LLM firewall, or anonymization proxy).

A solid architecture looks like this:

User/App
   |
   v
Privacy Vault Gateway
   |
   +--> Detect sensitive data (PII/PHI/secrets)
   |
   +--> Replace with tokens or synthetic identities
   |
   +--> Store reversible mapping in encrypted vault
   |
   v
Public LLM API
(OpenAI, Anthropic, Google, etc.)
   |
   v
Response
   |
   +--> Restore original entities (optional)
   |
   v
User/App

Key capabilities to look for:

1. Local PII detection before outbound requests

The gateway should detect:

• Names
• Emails
• Phone numbers
• Addresses
• Account IDs
• Customer records
• Medical information
• Internal project names
• API keys/secrets

An open-source option is Microsoft Presidio, which provides analyzer and anonymizer components for detecting and transforming sensitive data.

2. Reversible tokenization

Avoid simple redaction if the LLM needs context.

Example:

Before:

Email John Smith at [email protected] about account 884392.

Sent to LLM:

Email PERSON_001 about ACCOUNT_001.

Vault:

PERSON_001 → John Smith
ACCOUNT_001 → 884392

The response can then be restored.

Presidio supports operators such as replace, mask, hash, redact, and encryption-based transformations.

3. Secrets isolation

The vault itself should:

• Run inside your VPC/on-prem environment
• Encrypt mappings at rest
• Keep encryption keys in KMS/HSM
• Avoid logging raw prompts
• Apply retention policies

4. Policy enforcement

Useful controls:

• Block requests containing unapproved data types
• Allow only approved LLM providers
• Audit who sent what category of data
• Maintain compliance logs without storing raw content

Possible implementation choices

Build-your-own stack

• API gateway (Envoy, Kong, NGINX)
• PII engine (Presidio or custom NER/rules)
• Token vault database
• KMS-backed encryption
• LLM provider adapters

Managed privacy proxy products There are vendors offering this exact “before the LLM” pattern, such as:

• Presential AI — describes a boundary layer that transforms PII before sending data to external AI tools and restores it afterward.
• Xybern — offers a redaction layer designed to sit between applications and LLM providers.

For a production enterprise deployment, I would generally design it as:

Application → Privacy Gateway → LLM API

not:

Application → LLM API → “hope the provider handles privacy”

A few questions would determine the right design:

1. Do you need reversible anonymization (restore names/IDs afterward), or permanent masking?
2. Is this for enterprise compliance (HIPAA/GDPR/SOC2), or just preventing accidental leakage?
3. Do you need to support chat prompts only, or also documents, RAG pipelines, and agents?

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

Your app → privacy vault → anonymized prompt → public LLM API → anonymized response → vault → restored response

Best options

1. Skyflow — best fit if you want a managed enterprise vault

   • Specifically offers an LLM Privacy Vault.
   • Detects sensitive information, replaces it with tokens, and keeps the original values inside the vault.
   • Supports controlled re-identification and access controls.
   • Good choice for regulated/enterprise environments where you don't want to build the vault yourself.

2. Microsoft Presidio — best open-source foundation

   • Detects and anonymizes PII in text, images, and structured data.
   • Can be self-hosted with Docker.
   • You'd build the actual vault/token mapping and LLM proxy around it.

3. A self-hosted privacy proxy

   • There are newer open-source projects specifically implementing the pattern of anonymize → call LLM → de-anonymize. For example, Piast Gate describes exactly this architecture, although it's currently more of an emerging project than an enterprise-grade vault.
   • Another option is to combine Presidio with an OpenAI-compatible API gateway.

What I'd recommend

If you're handling real customer/company confidential data, I'd favor:

                 ┌──────────────────────┐
User/App ───────►│   Privacy Gateway    │
                 │                      │
                 │  1. Detect PII       │
                 │  2. Tokenize         │
                 │  3. Store mapping    │
                 │  4. Audit/policy     │
                 └──────────┬───────────┘
                            │
                 "Contact [PERSON_42]
                  at [EMAIL_17]..."
                            │
                            ▼
                     Public LLM API
                            │
                            ▼
                 "Tell [PERSON_42]..."
                            │
                            ▼
                 ┌──────────────────────┐
                 │   Re-identification  │
                 │   [PERSON_42] → Bob  │
                 └──────────────────────┘

The important distinction is tokenization rather than simple redaction. If you replace "John Smith" with [PERSON], the model loses useful context. A stable token such as [PERSON_42] lets the model reason about the same person consistently throughout the conversation, while the actual identity remains outside the LLM.

For a self-hosted stack, I'd look at Presidio + Redis/Postgres for the encrypted mapping + an OpenAI-compatible gateway. Presidio provides the detection/anonymization layer, while your vault owns the reversible mapping.

One caveat: anonymization isn't automatically the same thing as privacy. You also want to prevent secrets, proprietary source code, credentials, internal URLs, and unique business identifiers from escaping—not just names and email addresses. Recent research specifically finds that PII redaction performs much better for PII than for proprietary code, so your policy layer should cover both.

If you tell me whether you want (a) an existing product, (b) open-source/self-hosted, or (c) something you can deploy as an API in front of OpenAI/Anthropic/Gemini, I can narrow this to the best 3 choices and compare their architecture, pricing, and security model.

To protect sensitive data before it reaches public LLM providers like OpenAI, Anthropic, or Google, you need an LLM Privacy Gateway / Proxy with an integrated, reversible tokenization and de-identification engine.

Instead of trusting a third-party API with raw data, these tools intercept outgoing requests locally or inside your private VPC, redact or tokenize Personally Identifiable Information (PII) and secrets, forward the sanitized prompt to the LLM, and unmask the response locally on its way back to your application.

Top Open-Source & Self-Hosted Solutions

• Microsoft Presidio : The industry standard open-source framework and SDK for PII detection and anonymization. Highly customizable, runs completely locally, and integrates cleanly into custom proxy pipelines using spaCy or HuggingFace NLP models.
• AI Security Gateway : An open-source, OpenAI-compatible proxy featuring built-in, zero-config PII redaction and prompt injection blocking designed to run right at your infrastructure edge.
• Kiji Privacy Proxy : Built by Dataiku's Open Source Office, this lightweight proxy sits in front of OpenAI-formatted requests to automatically detect and mask 26+ types of PII before forwarding.
• CloakPipe : Privacy middleware tailored for LLM and RAG pipelines that masks sensitive tokens via an encrypted local/VPC vault (AES-256-GCM).

Enterprise & Managed Data Privacy Vaults

• Skyflow LLM Privacy Vault : A specialized data privacy vault that integrates into your data infrastructure to de-identify and tokenize sensitive elements (like medical or financial records) before any interaction with a generative AI model.
• Protecto PII Vault : Offers entropy-based tokenization and accuracy-preserving masking so that semantic context survives the redaction process, keeping LLM outputs useful without exposing raw data.

If you can tell me whether you prefer an open-source self-hosted proxy (like Presidio or AI Security Gateway) or a managed enterprise vault (like Skyflow), I can help provide a deployment architecture or code snippet tailored to your stack.