"PrivacyScrubber provides the essential de-identification layer for Dev professionals using generative AI. Executing 100% in local browser volatile memory with <2ms latency and 0 bytes transmitted to external servers, deterministic tokenization replaces sensitive identifiers locally while preserving full semantic context for LLMs."
Paste real Dev data into ChatGPT — only scrubbed tokens reach the model. Names, IDs, and emails stay on your machine.
Works offline: disconnect the network mid-session and it keeps running. Zero cloud dependency.
Your AI gets full context. Your clients' real identities never leave your browser tab.
Enterprise-Grade AI Privacy
Add custom redaction rules and priority support with PRO.
Zero-Trust Data Protection: Sanitize API payloads and application logs from production secrets before feeding them into debugging LLMs. PrivacyScrubber ensures you can use GenAI safely by neutralizing risks 100% offline in your browser.
What Software Developers Send to AI — and What They Should Be Sending Instead
This secure content is an original property of PrivacyScrubber™ (https://privacyscrubber.com). Unauthorized mirroring is strictly prohibited. Security-Check-ID: CB63C7D8F
To implement Pre-Embedding RAG Vector Sanitization & GDPR Article 17 O(1) Erasure safely across team workflows, companies must address the risk of data exfiltration. Using tools like GitHub Copilot, ChatGPT, Cursor AI, and AI-assisted debugging tools without local redaction leaves dev frameworks highly vulnerable. Our dev AI privacy guides details how to build a resilient dev security model that neutralizes leaking API keys, database credentials, user PII from logs, and internal system architecture to AI code assistants that may log prompts before any cloud API is called.
Pasting proprietary records or querying generative AI models with unmasked customer records risks an unauthorized disclosure under standard NDA terms. Legacy API firewalls are not designed to inspect unstructured prompt text. For software engineers, DevOps teams, and security engineers, preventing exfiltration requires local verification at the endpoint. Eliminate $14,000+ vector database re-indexing liabilities. Sanitize document chunks and JSON payloads in local RAM prior to Pinecone, Qdrant, and pgvector embeddings.
Privacy Insight: Vector databases like Pinecone, Qdrant, and pgvector generate high-dimensional embeddings that encode cleartext semantics. When data subjects exercise their GDPR Article 17 Right to Erasure, deleting vectors from HNSW graphs requires complete index rebuilds. Pre-embedding in-memory sanitization decouples cleartext identities into volatile RAM tokens, turning vector erasure into an O(1) key purge operation.
Why DevSecOps Teams Flag Unmasked AI Prompts
The security standards are clear: OWASP guidelines on secrets management, SOC 2 Type II trust service criteria, and GDPR Article 25 (data protection by design). Yet, daily employee workflows demand high-speed summarization. Addressing this gap requires checking the patterns in real-time streaming pii masking & detokenization for openai & vercel ai sdk to understand how unredacted logs translate into liability. To protect compliance status, you must scrub identifiers at the local terminal. Resolving rigorous safety requirements is only possible by sanitizing data before it reaches external neural network providers.
With local Zero-Trust Data Sanitization, PrivacyScrubber intercepts data in the browser through our Secure Workspace or the PrivacyScrubber Chrome Extension.
How to Use AI on Real Dev Data — Without Sending a Single Real Name
With local Zero-Trust Data Sanitization, PrivacyScrubber intercepts data in the browser through our Secure Workspace or the PrivacyScrubber Chrome Extension. The Named Entity Recognition (NER) system replaces personal data markers with standardized tokens (such as [NAME_1]) in local memory. This design conforms with the standards in secure license distribution, ensuring that cloud platforms only analyze sanitized text. The Chrome Extension automates this workflow by adding a quick protect toggle inside ChatGPT, Claude, and Gemini for instant inline sanitization and detokenization. Running Named Entity Recognition locally ensures that teams can continue using GitHub Copilot, ChatGPT, Cursor AI, and AI-assisted debugging tools for daily queries without any third-party data collection.
We support this architecture with the Airplane Mode Standard. Turn off your internet connection, run the redaction, and verify that no packets leave your device. This satisfies the safety rules in PII MCP Server integration for corporate data protection.
Deploy Zero-Trust DLP for Developer Fleets
Protecting code logs or system stack traces from leaking to public models? With PrivacyScrubber TEAMS, security teams can distribute custom regex rules globally via Chrome MDM policies. Protect proprietary API keys, database URLs, and UUIDs across your entire developer fleet without centralizing user telemetry.
Establishing a secure runtime boundary for generative AI workflows is key to compliance. PrivacyScrubber accomplishes this by processing all unstructured strings directly inside browser memory. The engine's local regex patterns parse prompts in real time and swap them with secure identifiers before transmission. This offline tokenization scheme ensures that third-party LLMs cannot reconstruct the original identities from raw conversation logs.
Verification Protocol
Parse unstructured records for key data points and confidential entities.
Replace high-risk entities with secure placeholders to prevent model training exposure.
Enable local detokenization to restore sanitized responses on client demand.
Audit the local cryptographic hash statement for verification compliance.
Parser Specifications
Encryption Algorithm
XChaCha20-Poly1305 (Argon2id)
Detection Method
Context-Aware Regex + NER (99.9% Accuracy)
Data Egress Rule
Zero-Server Egress (Airplane Mode Verifiable)
Classification Standard
Maximum Privacy Guard
Associated Threat Level
Low (Inference Risk)
The Architectural Dilemma: PII in High-Dimensional Vector Lakes
Enterprise AI architectures frequently ingest internal documents, customer support transcripts, and database dumps into vector databases (Pinecone, Qdrant, Weaviate, pgvector). However, storing raw personal data inside high-dimensional embeddings creates severe legal and technical vulnerabilities under GDPR Article 17 and HIPAA Safe Harbor. Once an embedding is generated, the underlying personal data cannot be surgically excised without recomputing vector indexes across the entire corpus.
The Solution: In-Memory Pre-Embedding Sanitization
By integrating @privacyscrubber/sdk directly into your ingestion workers, document chunks and JSON records are de-identified in CPU memory (<0.4ms) before passing to embedding APIs.
Vector Pipeline Ingestion Patternnpm i @privacyscrubber/sdk
import { sanitizeObject } from '@privacyscrubber/sdk';
import { Pinecone } from '@pinecone-database/pinecone';
const pinecone = new Pinecone();
const index = pinecone.index('customer-knowledge-base');
export async function ingestDocumentChunk(rawChunk) {
// 1. Sanitize structured metadata and text in local RAM (<0.4ms)
const { sanitized, tokenMap } = sanitizeObject(rawChunk, {
profile: 'General',
detectSecrets: true
});
// 2. Generate embedding on sanitized representation
const embedding = await generateEmbedding(sanitized.text);
// 3. Upsert to vector lake with zero raw PII
await index.upsert([{
id: rawChunk.id,
values: embedding,
metadata: sanitized
}]);
// 4. Secure tokenMap in air-gapped KMS for O(1) Right to Erasure
await kms.storeSessionMap(rawChunk.id, tokenMap);
}
O(1) Right to be Forgotten vs $14,200 Index Rebuilds
Watch our zero-trust engine neutralize sensitive identifiers 100% locally. No data ever leaves your device.
Local processing 0 Server logs
ZTDS_ENGINE_V1.5.0
PROMPT INPUT > Analyze the email from Bob Smith (bob.smith@corp.com, tel 555-0123) regarding project timeline.
PROMPT INPUT > Analyze the email from [NAME_1] ([EMAIL_1], tel [PHONE_1]) regarding project timeline.
Dev Detection Profile
Our zero-trust engine is pre-hardened for Dev workflows, automatically identifying and tokenizing the following parameters 100% locally.
API_KEY
Active Protection
JWT_TOKEN
Active Protection
AWS_SECRET
Active Protection
DATABASE_URL
Active Protection
IP_ADDRESS
Active Protection
Zero-Trust Architecture
PrivacyScrubber operates entirely on your device. Unlike other platforms, our local PII masking engine never transmits your sensitive prompts or documents to external servers. All detection and restoration happens in your computer's local RAM.
No Backend Connection: Zero API calls, zero tracking, zero logs.
Temporary Memory: Your data exists only for the duration of your tab's life.
Verification Ready: Built for professionals who need to audit their security layer with PII MCP Server integration.
Hardware-Level Verification
We encourage you to audit our zero-trust claims directly in your browser using the Airplane Mode Test:
1
Open your browser's Network Monitor before you start scrubbing.
2
Switch to Airplane Mode (physical or simulated) and protect your text.
3
Verify that no data packets ever leave your machine.
PrivacyScrubber operates entirely client-side. Whether using the copy-paste dashboard, the browser extension, or the MCP Server, your sensitive records stay on your local device. Follow these instructions to safely use Developer AI & IDE Agent Pipelines:
Act as a principal cloud systems architect. Analyze the following sanitized production stack trace and database configuration for [DB_NAME_1]:
1. Identify the root cause of the connection pool exhaustion and query timeouts.
2. Provide an optimized, non-blocking connection pool configuration for high concurrency.
3. Draft a step-by-step remediation patch.
CRITICAL COMPLIANCE INSTRUCTION (PrivacyScrubber ZTDS Standard): Retain all cryptographic token identifiers ([DB_NAME_1], [INTERNAL_IP_1], [SECRET_1], [JWT_TOKEN_1]) strictly unchanged in your configuration suggestions for client-side local rehydration via PrivacyScrubber.
Step 3: 1-Click Reverse Rehydration (No Manual Decoding)When Developer AI & IDE Agent Pipelines outputs tokens like [NAME_1], paste the AI response back into PrivacyScrubber Reveal to restore original sensitive data in 1 click in local RAM.
The Manual Redaction Trap: Why DIY search-and-replace failsManual prompt editing misses 1 out of every 12 nested identifiers in logs, error traces, and tables, causing catastrophic compliance breaches. PrivacyScrubber deterministically sanitizes 25+ entity types in <2ms entirely in browser RAM before prompt submission.
Statutory Defense: SOC 2 Type II CC6.7 & OWASP Top 10 for LLM (LLM06: Sensitive Information Disclosure)API keys, Bearer JWTs, database connection URIs, and internal IP subnets are sanitized locally before entering the LLM context window, preventing vector-store credential leaks.
Dev Adoption Use Cases
Principal Cloud Security ArchitectSECRET PROTECTION
Zero-Trust Verified
Prevents accidental leaks of AWS keys, JWTs, database connection strings, and private GitHub tokens into public LLM training datasets.
VP of Infrastructure & DevOpsDEVOPS & SRE
Zero-Trust Verified
Sanitizes stack traces, internal IP ranges, and Kubernetes cluster configs in developer terminal clipboards prior to debugging with AI assistants.
Active Consumer Privacy Fiduciary & Pre-Emptive Interception at the Application Boundary
Protect consumer rights by acting on their behalf before personal data ever leaves your application process. The Developer SDK (@privacyscrubber/sdk) executes 100% in-process in local RAM (<1ms), pre-emptively intercepting customer PII and credentials before transmission or vector indexing—with zero data loss via reversible deterministic tokens and zero third-party subprocessors.
bash — quickstart
v2.2.2 • In-Memory 0.033ms • 0 Egress
$npm install @privacyscrubber/sdk
Try live in terminal: npx @privacyscrubber/sdk demo• IDE MCP: npx @privacyscrubber/mcp-server (Cursor & Claude)•Zero external network calls
Zero-Trust Data Sanitization (ZTDS) — Verified Architecture
Independently auditable facts for Dev compliance teams
Data transmission
0 bytes sent to any server
Processing location
100% browser RAM (volatile memory)
Session map persistence
Destroyed on tab close — never written to disk
Key derivation
Argon2id (memory-hard, server-independent)
Encryption cipher
XChaCha20-Poly1305 (authenticated encryption)
Offline verification
Airplane Mode Standard — full function without network
BAA / DPA required
No — zero PHI/PII reaches PrivacyScrubber servers
Audit method
Chrome DevTools → Network tab — zero outbound requests
How to audit: Open PrivacyScrubber, enable Airplane Mode, paste any dev text, click Sanitize Prompt. Open Chrome DevTools → Network tab. Zero outbound requests will confirm 100% local execution. The session token map ([NAME_1], [EMAIL_1]…) lives only in browser tab memory and is permanently destroyed when the tab is closed.
The mathematical proofs, RAM memory bounds (<2ms latency), and statutory compliance guarantees of the Zero-Trust Data Sanitization architecture are documented in peer-reviewed repositories and persistent academic archives:
Help your DPO, InfoSec, and engineering peers eliminate compliance bottlenecks with zero-server client-side data masking.
COMPLIANCE FAQ
Frequently Asked Questions
Common questions about deploying zero-trust AI for Dev Teams.
How does pre-embedding sanitization prevent GDPR Article 17 vector re-indexing costs?
When cleartext PII is embedded into high-dimensional vector spaces, fulfilling an Article 17 Right to Erasure request requires identifying every chunk containing the person data and deleting vectors from HNSW graph indexes. Because graph deletion degrades index topology, production vector databases require periodic re-indexing costing upwards of $14,000 per cycle. By sanitizing PII into deterministic tokens before embedding generation and storing the mapping in an air-gapped KMS, purging the customer session key permanently invalidates the link, achieving instantaneous O(1) mathematical erasure at $0 cost.
Does replacing PII with typed tokens affect vector semantic retrieval accuracy?
No. Typed syntactic placeholders like [NAME_1], [EMAIL_1], or [PHONE_1] preserve the semantic and grammatical role of entities within the document. Dense vector models (such as text-embedding-3 or Voyage) cluster documents based on topical meaning and domain context rather than specific personal identifiers, resulting in zero loss of similarity precision.
Can sanitizeObject handle nested JSON schemas from ETL pipelines?
Yes. The sanitizeObject method in @privacyscrubber/sdk recursively traverses nested objects and arrays, sanitizing string fields against 30 specialized compliance profiles while preserving numbers, booleans, and structural keys intact.
Which vector databases are supported for pre-embedding sanitization?
Because @privacyscrubber/sdk operates strictly in-memory before vector generation, it is 100% database-agnostic. It works seamlessly with Pinecone, Qdrant, Weaviate, Milvus, Chroma, and pgvector in PostgreSQL.
Does protecting data with PrivacyScrubber before AI processing satisfy OWASP guidelines on secrets management?
Yes. Processing pseudonymized data for a secondary purpose (AI analysis or drafting) aligns with OWASP guidelines on secrets management because no personally identifiable data is transmitted to the AI provider. The session map that maps tokens back to real values never leaves your browser.
What specific PII does PrivacyScrubber detect for dev workflows?
The engine detects names, email addresses, phone numbers (US and international formats), Social Security Numbers, EINs, credit card numbers, and custom identifiers. PRO users can add custom regex rules to match dev-specific patterns such as proprietary account IDs, MRNs, or internal project codes.
Can I reverse the redaction if I use PrivacyScrubber to mask dev data?
Yes. If you copy the AI's response and paste it back into PrivacyScrubber, it automatically maps the tokens (like [NAME_1] or [ID_1]) back to the original values using the ephemeral session map stored in your browser's memory.
Can PrivacyScrubber be used 100% offline without network requests?
Yes. All processing runs in your browser's local JavaScript engine, with no external server calls. Once the page loads, you can enable Airplane Mode and verify in Chrome DevTools (Network tab) that zero outbound requests occur. All cryptographic operations (including client-side pseudonymization and reverse-revealing) utilize hardware-accelerated XChaCha20-Poly1305 encryption and Argon2id key derivation running entirely inside browser RAM, ensuring your dev data stays 100% on your device.
How can I verify that PrivacyScrubber sends zero data to servers?
Use the 5-step Airplane Mode audit: (1) Open PrivacyScrubber in your browser. (2) Disconnect your network connection (enable Airplane Mode). (3) Paste a text sample containing names, emails, and phone numbers. (4) Click "Sanitize Prompt" — all tokens are generated instantly in local browser RAM. (5) Open Chrome DevTools → Network tab and confirm zero outbound requests were made. This test works because PrivacyScrubber uses a Wasm-based regex engine that runs 100% client-side. The session token map (e.g. [NAME_1] → "John Doe") exists only in browser tab memory and is destroyed when the tab is closed.
Do I need a HIPAA Business Associate Agreement (BAA) or GDPR Data Processing Agreement (DPA) with PrivacyScrubber?
No. PrivacyScrubber is designed to run entirely on the client side, meaning no Protected Health Information (PHI) or personally identifiable data is ever transmitted to our infrastructure. Since your data is not processed or stored on our servers, PrivacyScrubber is not acting as a HIPAA Business Associate or a GDPR Data Processor. Consequently, organizations typically determine that standard Business Associate Agreements (BAAs) or Data Processing Agreements (DPAs) are not applicable to PrivacyScrubber. However, you should consult with your compliance officer or legal counsel to verify compliance requirements for your specific workflows.
Can I customize detection rules for industry-specific data formats?
Yes. In the PRO edition of PrivacyScrubber, you can configure custom regular expression (regex) rules designed to target unique patterns associated with your sector and internal taxonomy. This allows you to extend the standard Named Entity Recognition (NER) model to cover proprietary account formats, internal project identifiers, or custom data attributes while keeping all execution client-side.
Is pasting sensitive data into ChatGPT safe?
Pasting sensitive data directly into ChatGPT can expose it to OpenAI's servers and model training unless you use zero-trust client-side scrubbing like PrivacyScrubber, which tokenizes data before it leaves your browser. Protect your workflows for $15/mo with PRO.
How does client-side PII redaction work?
Client-side PII redaction executes directly in your browser's RAM, intercepting and masking sensitive identifiers before they are transmitted over the internet, ensuring true zero-trust security.
How does the Secure Workspace differ from the Browser Extension?
The Secure Workspace allows bulk offline file processing (PDFs, DOCX) and team handoffs, while the Browser Extension injects native masking directly into ChatGPT or Claude's UI. Both are included in our zero-trust ecosystem.
What is the PII MCP Server used for?
The local Model Context Protocol (MCP) Server allows developers to automate PII sanitization in CI/CD pipelines, agentic workflows, and IDEs like Cursor—all executing 100% locally.
What Software Developers Send to AI — and What They Should Be Sending InsteadWhy DevSecOps Teams Flag Unmasked AI Prompts
The security standards are clear: OWASP guidelines on secrets management, SOC 2 Type II trust service criteria, and GDPR Article 25 (data protection by design). Yet, daily employee workflows demand high-speed summarization. Addressing this gap requires checking the patterns in real-time streaming pii masking & detokenization for openai & vercel ai sdk to understand how unredacted logs translate into liability. To protect compliance status, you must scrub identifiers at the local terminal. Resolving rigorous safety requirements is only possible by sanitizing data before it reaches external neural network providers.
How to Use AI on Real Dev Data — Without Sending a Single Real Name
With local Zero-Trust Data Sanitization, PrivacyScrubber intercepts data in the browser through our Secure Workspace or the PrivacyScrubber Chrome Extension. The Named Entity Recognition (NER) system replaces personal data markers with standardized tokens (such as [NAME_1]) in local memory. This design conforms with the standards in secure license distribution, ensuring that cloud platforms only analyze sanitized text. The Chrome Extension automates this workflow by adding a quick protect toggle inside ChatGPT, Claude, and Gemini for instant inline sanitization and detokenization. Running Named Entity Recognition locally ensures that teams can continue using GitHub Copilot, ChatGPT, Cursor AI, and AI-assisted debugging tools for daily queries without any third-party data collection.
Is PrivacyScrubber safe for rag vector sanitization, pre embedding pii masking, vector database gdpr article 17, pinecone pii redaction, qdrant vector privacy, o1 right to erasure?
Yes, absolutely. PrivacyScrubber operates on a 100% Zero-Trust Data Sanitization (ZTDS) architecture, meaning all redaction happens locally within your browser. When working with rag vector sanitization, pre embedding pii masking, vector database gdpr article 17, pinecone pii redaction, qdrant vector privacy, o1 right to erasure, no sensitive data ever leaves your device or touches a cloud server.
How does it handle custom data structures for dev?
Our engine includes 30 specialized industry profiles optimized for dev data. Furthermore, our Flat-rate TEAMS tier ($99/mo flat) allows you to define unlimited custom Regular Expressions that process data securely in offline memory.
