@privacyscrubber/mcp-server

CISO-Approved Zero-Trust PII & Secrets Redaction MCP Server for Cursor, Windsurf, and Claude Desktop. Locally scrubs PII, secrets, credentials, and custom regex rules from files and text contexts before they reach remote LLM providers to prevent API leaks and ensure HIPAA/SOC 2 compliance at the developer endpoint.

Support Zero-Trust Open Source: If PrivacyScrubber protects your API keys, source code, and developer credentials from model leaks, please Star this repository on GitHub or execute gh repo star moxno/privacyscrubber-mcp.

Direct Commercial Licensing & Production Infrastructure:
Looking to automate zero-trust PII redaction inside backend microservices, ETL pipelines, or LangChain/LlamaIndex agents?

• Developer SDK ($299/mo flat or $2,990/yr): Unlimited internal backend nodes, zero network egress, <1ms in-memory latency. Subscribe to Developer SDK | Technical Specs
• TEAMS ($99/mo flat): Unlimited team seats across your entire domain with cryptographic P2P session handoff. Deploy TEAMS Plan
• PRO ($15/mo or $110 Lifetime): Single-seat unlimited throughput across all 30 vertical profiles. Get PRO License

---

🔒 Zero-Trust Data Flow

All sensitive parameters, identifiers, and variables are intercepted locally inside your machine's RAM. They are replaced by tokens (e.g. [EMAIL_1]) before being sent to the AI. Once the AI responds, the tokens are safely swapped back to original values in your local context.

[Raw Input / Files] ──> [MCP sanitize_text] ──> [Masked Tokens] ──> [LLM API]
                               │                                       │
                        (In-Memory Map)                             (Result)
                               │                                       │
[Original Output] <─── [MCP reveal_text] <─────────────────────────────┘

---

🚀 Installation

1. Install via Smithery

To automatically configure and run with your preferred client, install using Smithery:

npx -y @smithery/cli install @privacyscrubber/mcp-server --write-to-clients

2. Instant Run with NPX

Run the server directly without local installation:

npx -y @privacyscrubber/mcp-server

3. Instant Run with UVX (Python / PyPI)

Run the server directly inside Python agent workflows (CrewAI, LangChain, AutoGen, LlamaIndex):

uvx privacyscrubber-mcp

Or via pip:

pip install privacyscrubber-mcp
privacyscrubber-mcp

4. Programmatic Node.js / TypeScript SDK (Lightweight Presidio Alternative)

Need direct, in-memory zero-trust PII sanitization in your backend microservice, Next.js app, or RAG vector pipeline rather than an MCP server? Use our official zero-dependency SDK:

npm install @privacyscrubber/sdk

import OpenAI from 'openai';
import { wrapOpenAI } from '@privacyscrubber/sdk';

// Transparently masks PII before sending to LLM and rehydrates responses:
const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY }));

// Outbound prompt is sanitized in local RAM before leaving your machine:
// "Schedule a call with [NAME_1] at [EMAIL_1] regarding API key [AWS_KEY_1]."
const completion = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Schedule a call with Alice Smith at alice@acme.com with AKIAIOSFODNN7EXAMPLE.' }]
});

// Incoming LLM answer is automatically rehydrated with "Alice Smith (alice@acme.com)":
console.log(completion.choices[0].message.content);

⚡ Why @privacyscrubber/sdk vs Microsoft Presidio?

Microsoft Presidio is the Python standard, but deploying it in a Node.js / TypeScript stack requires running heavy Python microservices, Docker containers, and 500MB+ spaCy NLP models with 35–120ms latency. @privacyscrubber/sdk runs 100% in-process with zero dependencies:

Feature / Metric	@privacyscrubber/sdk	Microsoft Presidio	AWS Comprehend / Google Cloud DLP
Runtime & Dependencies	0 dependencies (~150KB)	Python + Docker + spaCy (~500MB)	Heavy Cloud SDKs
Execution Latency	Sub-millisecond in RAM (<1ms)	35–120ms HTTP/gRPC roundtrip	180–400ms external cloud roundtrip
Docker / Sidecar Needed	None (Pure in-process Node/WASM)	Mandatory Docker container	VPC endpoints & IAM configurations
Network Egress	0 Bytes (100% Air-gapped)	Local internal network hop	Full unencrypted payload to cloud
Data Loss & Reversibility	0 Loss (RAM-reversible via restore())	Manual token vault configuration	Irreversible masking / hashing
OpenAI / LangChain 1-Liner	Built-in (wrapOpenAI, wrapAiStream)	Complex custom pipeline glue	Custom proxy architecture
DevOps Secrets Interception	Built-in (AWS, JWT, DB URIs, GitHub PAT)	Custom regex recognizers needed	Cloud-specific classifiers
Streaming Rehydration	Built-in (wrapAiStream, TransformStream)	Buffering / chunk split failures	Not supported in real-time streams

👉 View @privacyscrubber/sdk on NPM | Full documentation, Express middleware, LangChain transforms, and 25 compliance profiles.

🤖 AI Orchestrator Recipes: LangChain, LlamaIndex, CrewAI & AutoGPT

If you are building autonomous agents, RAG vector pipelines, or backend services rather than single-user IDE prompts, use @privacyscrubber/sdk to sanitize data in-memory:

1. LangChain.js (LCEL & RAG Document Ingestion)

import { ChatOpenAI } from '@langchain/openai';
import { createLangChainTransform, createDocumentTransformer } from '@privacyscrubber/sdk';

// A. RAG Pre-Ingestion: sanitize documents before embedding into vector stores
const docTransformer = createDocumentTransformer({
  profile: 'General',
  sensitiveMetadataKeys: ['account_owner', 'submitter_email'],
  attachTelemetry: true
});
const sanitizedDocs = await docTransformer.transformDocuments(rawDocs);

// B. LCEL Runtime Chains: sanitize prompts and restore responses in local RAM
const transform = createLangChainTransform({ defaultProfile: 'Dev' });
const { scrubbedText, tokenMap } = transform.preprocess(
  "Deploying database with user admin and pwd postgresql://user:SecretPass123@db.internal:5432/prod"
);
const model = new ChatOpenAI({ model: 'gpt-4o' });
const response = await model.invoke(scrubbedText);
const finalOutput = transform.postprocess(response.content, tokenMap);

2. LlamaIndex.TS & Universal Vector DB Ingestion (Chroma, Pinecone, Qdrant)

import { createVectorIngestionGuard, createLlamaIndexTransform } from '@privacyscrubber/sdk';

// Initialize in-memory Vector Ingestion Guard (<1ms per batch, zero network egress)
const guard = createVectorIngestionGuard({
  profile: 'Finance',
  sensitiveMetadataKeys: ['contractor_email', 'billing_contact']
});

// A. Sanitize Chroma columnar batches ({ ids, documents, metadatas })
const cleanChroma = guard.sanitizeRecords(chromaBatch);

// B. Sanitize Pinecone / Qdrant record arrays ({ id, text/payload, metadata })
const cleanRecords = guard.sanitizeRecords(pineconeRecords);

// C. Align search query tokens with sanitized vector space before embedding
const { query: alignedQuery } = guard.sanitizeQuery("Search user john@example.com records");

// D. Transparent Vector Store Proxy: auto-sanitizes on write, re-hydrates on query
const guardedStore = guard.wrapVectorStore(nativeVectorStore);

Complete runnable recipes are included inside the npm package under @privacyscrubber/sdk/examples/ (or run npx @privacyscrubber/sdk).

3. CrewAI & Multi-Agent Swarms (Sidecar Mode / Python Interop)

# Run lightweight zero-dependency local daemon (<1ms in-memory, 0 external network calls):
npx @privacyscrubber/sdk sidecar --port 3100

# In your Python CrewAI / AutoGen Agent tools:
import requests

def sanitize_agent_input(prompt: str) -> tuple[str, dict]:
    res = requests.post("http://127.0.0.1:3100/sanitize", json={"text": prompt, "profile": "Dev"}).json()
    return res["scrubbedText"], res["tokenMap"]

def restore_agent_output(ai_response: str, token_map: dict) -> str:
    res = requests.post("http://127.0.0.1:3100/restore", json={"text": ai_response, "tokenMap": token_map}).json()
    return res["restoredText"]

4. AutoGPT / Multi-Turn Autonomous Agents

import { PrivacyScrubberEngine, createGuardedTools } from '@privacyscrubber/sdk';

const engine = new PrivacyScrubberEngine({ defaultProfile: 'Dev' });
// Equips agent with tools that intercept commands, file reads, and git diffs before model exposure:
const guardedTools = createGuardedTools(engine);

Enterprise & Production Licensing:

• Self-Serve Developer SDK ($299/mo flat or $2,990/yr): Unlimited internal backend nodes, microservices, and RAG pipelines. Subscribe to Developer SDK | Developer SDK Specs
• PrivacyScrubber TEAMS ($99/mo flat): Unlimited team seats, centralized policy enforcement, encrypted session handoff. Deploy TEAMS Plan
• Enterprise Air-Gapped License: On-premise source code distribution, zero-network custom models. Contact Enterprise

🛡️ Architecture & Security Deep-Dive (Zero-Trust vs Cloud DLP)

When AI IDEs (Cursor, Claude Desktop, Windsurf) connect to model providers, developer credentials, database connection strings, and internal customer PII are at continuous risk of prompt exfiltration. The @privacyscrubber/mcp-server enforces four immutable architectural guarantees:

1. Stdio Air-Gapped Transport: The MCP server communicates exclusively over local standard input/output (stdio) child processes spawned by your IDE. It opens zero external listening ports and initiates zero remote network requests.
2. Volatile RAM-Only Token Map: The mapping table between synthetic tokens ([AWS_KEY_1], [EMAIL_1]) and raw cleartext is maintained exclusively in ephemeral node memory and is wiped the moment your IDE session closes.
3. Deterministic AST Lookarounds vs Cloud Proxy Overhead: Unlike cloud DLP gateways (Nightfall, Skyflow) that add 200–400ms latency and transmit unencrypted code to third parties, @privacyscrubber/mcp-server runs locally in <2ms with zero data egress.
4. Permanent Standards & Academic Validation:
   • IETF Specification: draft-sibiryakov-ztds-protocol-01 — Revision 01 · 2026-09-23 · 13 pages
   • CERN / Zenodo Foundation: DOI 10.5281/zenodo.22058770
   • Center for Open Science (OSF): DOI 10.17605/OSF.IO/5BYJF
   • Patent Pending: U.S. & International Patents Pending (Paris Convention / PCT Priority)

---

⚙️ Client Integrations

Claude Desktop

Add this to your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "privacyscrubber": {
      "command": "npx",
      "args": ["-y", "@privacyscrubber/mcp-server"],
      "env": {
        "PRIVACYSCRUBBER_KEY": "YOUR_OPTIONAL_PRO_LICENSE_KEY"
      }
    }
  }
}

Cursor / Windsurf

1. Navigate to Settings -> Features -> MCP.
2. Add new MCP server:
   • Name: privacyscrubber
   • Type: command
   • Command: npx -y @privacyscrubber/mcp-server
3. Optional: Set PRIVACYSCRUBBER_KEY as an environment variable in your system shell.

Cline / Roo Code

Add to cline_mcp_settings.json:

{
  "mcpServers": {
    "privacyscrubber": {
      "command": "npx",
      "args": ["-y", "@privacyscrubber/mcp-server"],
      "env": {
        "PRIVACYSCRUBBER_KEY": "YOUR_OPTIONAL_PRO_LICENSE_KEY"
      }
    }
  }
}

Claude Code CLI

Add directly from your terminal:

claude mcp add privacyscrubber -- npx -y @privacyscrubber/mcp-server

---

🛠️ Provided Tools & JSON-RPC Specifications

1. sanitize_text

Redacts PII, secrets, API keys, and credentials from a text block and populates the volatile local replacement mapping.

• Arguments:
  • text (string, required): The raw content or logs to sanitize.
  • profile (string, optional): Gated industry detection profile (e.g., 'General', 'Dev', 'Medical', 'Legal', 'Compliance'). Defaults to 'General'.
• JSON-RPC Call Example:

  {
    "method": "tools/call",
    "params": {
      "name": "sanitize_text",
      "arguments": {
        "text": "Contact me at dev-key-1234 or jane.doe@company.com",
        "profile": "General"
      }
    }
  }
  

• Response Example:

  {
    "content": [
      {
        "type": "text",
        "text": "Contact me at [SECRET_1] or [EMAIL_1]"
      }
    ]
  }
  

2. reveal_text

Detokenizes the AI response back to the original values locally.

• Arguments:
  • text (string, required): The response from the LLM containing tokenized placeholders.
• JSON-RPC Call Example:

  {
    "method": "tools/call",
    "params": {
      "name": "reveal_text",
      "arguments": {
        "text": "Please reach out to [EMAIL_1] regarding the update."
      }
    }
  }
  

• Response Example:

  {
    "content": [
      {
        "type": "text",
        "text": "Please reach out to jane.doe@company.com regarding the update."
      }
    ]
  }
  

3. sanitize_file

Reads a local file, extracts text, sanitizes it, and returns the redacted template for LLM analysis.

• Supported Formats: Plain text (source code, logs, CSV, JSON, markdown) and Microsoft Word (.docx) documents.
• Arguments:
  • filePath (string, required): Absolute file path to read and sanitize.
  • profile (string, optional): The industry detection profile.

4. guard_exec (Command Execution Firewall)

Safely executes terminal commands in an isolated child process, masking stdout/stderr PII, database credentials, and API keys in local RAM before passing them to the AI agent. Includes a CISO audit receipt in stderr.

• Arguments:
  • command (string, required): The shell command to execute (e.g. cat .env, docker logs web, git diff).
  • cwd (string, optional): Working directory.
  • profile (string, optional): Detection profile (defaults to Dev).
  • timeout_ms (number, optional): Timeout in ms (defaults to 15000).

5. guard_read_file (Credential-Masking File Reader)

Reads files (.env, configs, source code, database dumps) and tokenizes all passwords, JWTs, and PII in volatile memory, returning safe redacted content for AI reasoning.

• Arguments:
  • file_path (string, required): Path to file.
  • profile (string, optional): Detection profile (defaults to Dev).
  • max_lines (number, optional): Line cap for large files (defaults to 500).

6. guard_git_diff (Pre-Commit & Diff Sanitizer)

Inspects staged (--cached) or unstaged repository diffs, redacting any newly introduced secrets or PII in local RAM before AI code review or commit message generation.

• Arguments:
  • staged (boolean, optional): If true, inspects staged changes (git diff --cached). Defaults to false.
  • cwd (string, optional): Working directory.
  • profile (string, optional): Detection profile (defaults to Dev).

7. guard_apply_patch (Safe Patch Applicator)

Reverses token placeholders ([API_KEY_1], [SECRET_1]) in AI-generated code or text by looking up the local RAM session map, creating a .bak backup, and writing authentic cleartext directly to disk. The remote LLM never sees real secrets.

• Arguments:
  • file_path (string, required): Path to target file.
  • content (string, required): Content containing tokens to restore on disk.
  • create_backup (boolean, optional): Backup existing file before write (defaults to true).

8. create_agent_rules (1-Click Agent Rule Injection)

Automatically scaffolds CISO-grade Zero-Trust directives into .cursorrules, .windsurfrules, CLAUDE.md, .github/copilot-instructions.md, or .clinerules.

• Arguments:
  • agent_types (array, optional): ["all"], ["cursor"], ["windsurf"], ["claude_code"], ["copilot"], ["cline"]. Defaults to ["all"].
  • workspace_dir (string, optional): Workspace directory.

9. check_status

Returns a visual dashboard showing your current tier, session request count, active profiles, and upgrade instructions. Use it at any time to check your license status or get setup help.

• Arguments: (none required)
• JSON-RPC Call Example:

  {
    "method": "tools/call",
    "params": { "name": "check_status", "arguments": {} }
  }
  

• Response Example (Free Tier):

  ╔══════════════════════════════════════════════════╗
  ║       PrivacyScrubber MCP Server v2.2.8          ║
  ╠══════════════════════════════════════════════════╣
  ║  🔓 Tier: FREE                                   ║
  ║  📊 Session requests: 5                          ║
  ║  📁 Input size limit: 15,000 characters/request  ║
  ╠══════════════════════════════════════════════════╣
  ║  🏷️  Profiles: General only — PRO unlocks 25 more ║
  ║  📋 Custom rules: 🔒 Locked — requires PRO       ║
  ╠══════════════════════════════════════════════════╣
  ║  💳 Upgrade to PRO — $110 Lifetime               ║
  ║     https://privacyscrubber.com/pricing?utm_source=npm&utm_medium=readme&utm_campaign=mcp_server          ║
  ╠══════════════════════════════════════════════════╣
  ║  After purchase, add your key to MCP config:     ║
  ║  "PRIVACYSCRUBBER_KEY": "<your-key-here>"        ║
  ║  Full setup guide:                               ║
  ║  https://privacyscrubber.com/pii-mcp/?utm_source=npm&utm_medium=readme&utm_campaign=mcp_server          ║
  ╚══════════════════════════════════════════════════╝
  

---

🛡️ Standalone CLI: ps-guard

PrivacyScrubber bundles ps-guard for Unix pipe, pre-commit, and agentic workflows:

# Pipe any output through RAM redaction
cat .env | npx ps-guard --profile dev

# Execute commands through the ZTDS safety wrapper
npx ps-guard -- npm test

# Review git diff with secrets redacted
npx ps-guard --diff --staged

# Generate rules for all AI IDEs (.cursorrules, .windsurfrules, CLAUDE.md, copilot, cline)
npx ps-guard --rules

---

🌐 Browser Extension & Web Client

Looking for real-time protection directly inside your web browser?

• Chrome Extension: Get the PrivacyScrubber Chrome Extension to sanitize prompts directly inside ChatGPT, Claude, and Gemini in real-time.
• Web Sandbox: Use the zero-server browser sanitization tools at PrivacyScrubber Homepage.

📄 License & Commercial Upgrade

By default, the server runs under the Free Tier (restricted to 15,000 characters per request and the basic General PII profile). To unlock 30 specialized engineering, medical, legal, and financial PII profiles, as well as team-wide custom rules, you can purchase a commercial license.

Commercial Plan & Feature Comparison

Feature	Community Free	PRO Tier	TEAMS Tier	Developer SDK
Execution Architecture	Local Stdio RAM	Local Stdio RAM	Local Stdio RAM	In-Process Node/WASM
Volatile Tokenization	Yes (RAM-only)	Yes (RAM-only)	Yes (RAM-only)	Yes (RAM-only)
Standard PII Masking	Yes	Yes	Yes	Yes
Max Character Throughput	15,000 chars / call	Unlimited	Unlimited	Unlimited
30 Industry Profiles	General Only (5k trial)	All 30 Profiles	All 30 Profiles	All 30 Profiles
Custom Regex Rules	Locked	Unlimited	Unlimited	Unlimited
Team Rules Sync (GPO)	No	No	Yes (Shared Link)	Yes (Configurable)
Microservice / RAG Export	No	No	No	Yes (@privacyscrubber/sdk)
Subprocessor Liability	0 (Client-side)	0 (Client-side)	0 (Client-side)	0 (In-Process Node/WASM)
Licensing Cost	$0	$15/mo or $110 Lifetime	$99/mo Flat Rate	$299/mo or $2,990/yr
Direct Activation	Default included	Acquire PRO	Deploy TEAMS	Subscribe SDK

👉 Acquire a Commercial License Key at privacyscrubber.com/pricing

---

🔐 After Purchase: Activate PRO in Your MCP Client

After purchasing a PRO license at privacyscrubber.com/pricing, you will receive a license key. Add it to your MCP client config as an environment variable: PRIVACYSCRUBBER_KEY.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "privacyscrubber": {
      "command": "npx",
      "args": ["-y", "@privacyscrubber/mcp-server"],
      "env": {
        "PRIVACYSCRUBBER_KEY": "YOUR_LICENSE_KEY_HERE"
      }
    }
  }
}

Restart Claude Desktop after saving.

Cursor

1. Go to Settings → Features → MCP Servers.
2. Find privacyscrubber and click Edit.
3. Add the environment variable: PRIVACYSCRUBBER_KEY=YOUR_LICENSE_KEY_HERE.
4. Restart Cursor.

Alternatively, export it system-wide so all tools pick it up:

# macOS / Linux — add to ~/.zshrc or ~/.bashrc
export PRIVACYSCRUBBER_KEY="YOUR_LICENSE_KEY_HERE"

Windsurf

Edit ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "privacyscrubber": {
      "command": "npx",
      "args": ["-y", "@privacyscrubber/mcp-server"],
      "env": {
        "PRIVACYSCRUBBER_KEY": "YOUR_LICENSE_KEY_HERE"
      }
    }
  }
}

Verify Activation

After adding the key, ask your AI agent to call check_status:

Use the check_status tool from PrivacyScrubber MCP

---

🔗 Ecosystem & Production Architecture Guides

• 🌐 Web App: https://privacyscrubber.com
• 📦 Node.js / TypeScript SDK: @privacyscrubber/sdk (npm)
• 🧩 Chrome Extension: Chrome Web Store
• 🛡️ RAG & Vector Databases: Sanitizing PII Before Vector DB Ingestion (Pinecone, Chroma, Qdrant)
• 🤖 LangChain & LlamaIndex: In-Memory PII Middleware for AI Agent Pipelines
• ⚡ AWS Comprehend Alternative: In-Memory Redaction Without Egress or Cloud Overhead

---

📚 Academic Foundations & Regulatory Verification

PrivacyScrubber and the Zero-Trust Data Sanitization (ZTDS) protocol are backed by published scientific, clinical, and legal treatises:

Repository / Archive	DOI / Identifier	Focus Area	Regulatory & Compliance Scope
IETF Specification	draft-sibiryakov-ztds-protocol	The ZTDS Protocol for Frontier AI Ingestion (Internet-Draft)	Global AI Privacy, Zero-Egress Architecture
Zenodo / CERN	10.5281/zenodo.22058770	Zero-Trust Data Sanitization (ZTDS) Protocol Foundation	Cross-Border AI Privacy, ISO 27001 A.8.11
OSF (Center for Open Science)	10.17605/OSF.IO/5BYJF	Empirical Latency Benchmark & Memory Profiling (<2ms RAM)	Performance vs Cloud DLP Proxies
SSRN / Elsevier	SSRN ID: 7335581	Enterprise Generative AI Governance	EU AI Act, UK GDPR, US State Privacy
medRxiv (Cold Spring Harbor)	MEDRXIV/2026/361661	Multi-Center Clinical Trial De-Identification	HIPAA Safe Harbor Section 164.514(b)
Law Archive / OSF	LawArchive ID: 4wc86	Preserving Attorney-Client Privilege in AI Workflows	ABA Model Rules & Legal Ethics

Citing PrivacyScrubber in Research & Audits

@software{sibiryakov2026privacyscrubber,
  author = {Sibiryakov, Ilya},
  title = {PrivacyScrubber: Zero-Trust Data Sanitization (ZTDS) Engine & MCP Server},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.22058770},
  url = {https://github.com/moxno/privacyscrubber-mcp}
}

---

📄 License

MIT © Ilya Sibiryakov (BrandMeWeb)

---

⚖️ Intellectual Property & Patent Disclosures

The Zero-Trust Data Sanitization (ZTDS) architecture, in-memory deterministic tokenization, cryptographic session handoff, and stdio execution methods implemented in this package are proprietary technology of Ilya Sibiryakov (BrandMeWeb) and are protected under Patent Pending status:

• Legal Status: U.S. & International Patents Pending
• Priority Framework: Paris Convention Art. 4 & 35 U.S.C. § 119 Priority (locked through September 14, 2027)
• Filing / Priority Date: September 14, 2026
• Official Title: SYSTEM AND METHOD FOR CLIENT-SIDE ZERO-TRUST DATA SANITIZATION AND CRYPTOGRAPHIC SESSION HANDOFF IN ARTIFICIAL INTELLIGENCE WORKFLOWS
• Patent Disclosures Hub: privacyscrubber.com/patents/ (formal verification via counsel NDA).

🌐 Internet Engineering Task Force (IETF) Specification

• Specification Title: The Zero-Trust Data Sanitization (ZTDS) Protocol for Frontier Artificial Intelligence Ingestion
• Document Category: IETF Internet-Draft (Individual Submission)
• IETF Datatracker: https://datatracker.ietf.org/doc/draft-sibiryakov-ztds-protocol/
• Archive Plaintext: https://www.ietf.org/archive/id/draft-sibiryakov-ztds-protocol-01.txt