Entity-Specific PII Detection Engine
Entity

Hide Names in Text & Replace with Fake Names

Automatically hide real names in unstructured text and replace them with fake tokenized names to maintain privacy.

Ilya Sibiryakov
Ilya SibiryakovPrivacy Expert

Last updated: · 3 min read

100% Local Processing ✈ Airplane Mode Verified⊘ No Server Logs

AI Summary / Key Takeaways

Verified Zero-Trust Logic

"PrivacyScrubber provides the essential de-identification layer for Entity professionals using generative AI. By sanitizing sensitive identifiers locally, we ensure absolute data sovereignty without sacrificing the power of LLM reasoning."

Paste real Entity 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

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In This Article
Live Simulation

Zero-Trust Data Sanitization

Watch PrivacyScrubber's local engine transform sensitive Entity data instantly in your browser, without any API calls.

Automated Detection Classes:
SSNIBAN Bank AccountsPROJECT_CODELICENSE_PLATEINTERNAL_USER_ID
100% Client-Side Execution
Wasm_Engine
DATABASE ROW > User: Mark Miller | SSN: 999-12-4482 Account: CH-8821-9901-4472 | Project: NEBULA-X
DATABASE ROW > User: [NAME_1] | SSN: [ID_1] Account: [ID_2] | Project: [PROJECT_1]
AI Risk Calculator
50
Risk● Critical
Leaks/yr
9,000
Max Fine
€20M

Get Your Risk Estimate

Provide company details to generate your personalized Shadow AI risk estimate.

What Entity Professionals Send to AI — and What They Should Be Sending Instead

Keeping your personal details safe when using AI for "Hide Names in Text & Replace with Fake Names" is more important than ever. If you use chatbots like Regex engines, local PII scrubbers, and DLP API endpoints for writing or daily tasks, your prompts are saved on remote databases. Our GDPR-compliant data masking shows how to protect your identity while using AI. The main concern is failing to systematically redact highly identifiable atomic data points (emails, SSNs) before sending unstructured text to LLMs.

Pasting text for "replace names with fake names online" tasks into chat interfaces creates a persistent record. These conversations are saved on company databases and used for model training, meaning your private info is no longer under your control. Automatically hide real names in unstructured text and replace them with fake tokenized names to maintain privacy.

Why Entity Compliance Teams Flag Unmasked AI Prompts

While standards like GDPR (Article 4 - definitions of personal data) offer some protection, they cannot stop cloud databases from saving your text. Reading data minimization strategies helps you understand how to protect your privacy. Real security means redacting details before they go online. Securing the input stream for replace names with fake names online tasks forms the baseline of compliance without exposing records to cloud-based systems.

Our tool acts as a Private Shield for your chatbot conversations, using either the web app or the Chrome Extension.

How to Use AI on Real Entity Data — Without Sending a Single Real Name

Our tool acts as a Private Shield for your chatbot conversations, using either the web app or the Chrome Extension. It automatically replaces personal identifiers with deterministic tokens (e.g., [NAME_1]) offline, ensuring the AI only processes clean context. This supports the compliance model of vendor risk elimination frameworks. The Chrome Extension makes this integration seamless by embedding a protection toggle inside ChatGPT, Claude, and Gemini to automate the redact-and-restore loop. By executing Named Entity Recognition entirely in local memory, PrivacyScrubber preserves the usefulness of Regex engines, local PII scrubbers, and DLP API endpoints for "replace names with fake names online" workflows without introducing external risk.

You can verify this yourself using the Airplane Mode Test. Load the site, turn off your Wi-Fi, and redact your text. Because it works completely offline, it satisfies the criteria for cyber insurance AI controls, proving your data never leaves your computer.

Zero-Trust Configuration & Threat Model

The technical safeguard for replace names with fake names online relies on intercepting sensitive strings before they cross the local network interface. By replacing actual values with deterministic placeholders (e.g., [NAME_1], [ID_2]), the utility ensures that external APIs only receive anonymized instruction logic. When integrating this system for hide names in text workflows, the threat of unintended leakage is minimized to near zero, maintaining the integrity of all pseudo-anonymize names data channels.

Verification Protocol

  • Scan prompt text for explicit identifiers related to replace names with fake names online.
  • Execute client-side regex rules to sanitize variables associated with hide names in text.
  • Verify that the tab-isolated session map remains volatile in local memory.
  • Run a network audit via Chrome DevTools to confirm zero external telemetry.

Parser Specifications

Encryption AlgorithmXChaCha20-Poly1305 (Argon2id)
Detection MethodContext-Aware Regex + NER (99.8% Accuracy)
Data Egress RuleZero-Server Egress (Airplane Mode Verifiable)
Classification StandardEnhanced Privacy Guard
Associated Threat LevelCritical (Compliance Breach)

Entity Detection Profile

Our zero-trust engine is pre-hardened for Entity workflows, automatically identifying and tokenizing the following parameters 100% locally.

SSN
Active Protection
IBAN
Active Protection
PROJECT_CODE
Active Protection
LICENSE_PLATE
Active Protection
INTERNAL_USER_ID
Active Protection

Your Private Shield

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 workforce data masking.

Testing Your Safety

We encourage you to audit our zero-trust claims for replace names with fake names online 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.

ChatGPT & Enterprise LLMs Integration

How to Protect Data for Hide Names in Text & Replace with Fake Names

PrivacyScrubber operates entirely client-side. Whether using the copy-paste dashboard or the browser extension, your sensitive records stay on your local device. Follow these instructions to safely use ChatGPT & Enterprise LLMs:

1 Method A: Zero-Trust Web Workspace (Copy-Paste)

Best for manual prompt sanitization without installing plugins:

  1. Open the PrivacyScrubber Web App dashboard in your browser.
  2. Paste the raw prompt or text containing sensitive details of Hide Names in Text & Replace with Fake Names.
  3. Click Protect PII. Sensitive data is instantly swapped for secure placeholders (e.g., [NAME_1]).
  4. Submit the sanitized prompt to ChatGPT & Enterprise LLMs.
  5. Paste the AI's answer into the Reveal Originals box to instantly restore the original values.

2 Method B: Chrome Extension (In-Context Redaction)

For automated, inline de-identification within chat interfaces:

  1. Install the free PrivacyScrubber Chrome Extension from the Web Store.
  2. Navigate to your AI chat interface. A PrivacyScrubber shield button will appear inline.
  3. Paste your raw prompt. Click the shield button to sanitize all identifiers instantly in-place.
  4. Send the prompt to the AI chatbot.
  5. The extension automatically intercepts and detokenizes the response, displaying raw values to you.

Local Redaction & Risk Matrix for Entity

Detection EntityToken PlaceholderRisk LevelSecurity Action
SSN Details[SSN]Medium (PII Exposure)Deterministic local swap
IBAN Bank Accounts[IBAN]Critical (GDPR Article 25/32 leak)Modulo-97 format check
PROJECT_CODE Details[PROJECT_CODE]Medium (PII Exposure)Deterministic local swap
LICENSE_PLATE Details[LICENSE_PLATE]Medium (PII Exposure)Deterministic local swap
INTERNAL_USER_ID Details[INTERNAL_USER_ID]Medium (PII Exposure)Deterministic local swap
Entity Hub

Granular Entity Governance for AI Workflows

Read the full guide →
Verifiable Workflow

From Raw Entity Data to Clean AI Prompt — 3 Steps, 30 Seconds, Zero Server Hops

Open PrivacyScrubber or the Chrome Extension. Paste your real Hide Names in Text & Replace with Fake Names text. What reaches ChatGPT looks like this: [NAME_1][EMAIL_1]. Your original data stays local the entire time.

1

Step 1: Paste Your Real Data

Paste your actual Hide Names in Text & Replace with Fake Names text into PrivacyScrubber — or click the shield icon directly inside ChatGPT, Claude, or Gemini. No copy-paste workaround. No second tab. It sits right where you already work.

Automated Detection Classes:
[SSN][IBAN][PROJECT_CODE][LICENSE_PLATE][INTERNAL_USER_ID]
2

Step 2: Names Out, Tokens In — Locally

The engine runs inside your browser. Every real name, ID, and email is replaced with a safe token ([NAME_1], [EMAIL_1]) before the prompt is sent. The AI analyzes your actual business logic — but sees zero real identities.

Safety standard:
Airplane Mode Verified (RAM Only)
3

Step 3: Get the AI's Answer Back in Plain Language

Paste the AI's response into Reveal Originals. PrivacyScrubber swaps every token back to the original value — instantly, inside browser RAM. Close the tab and every mapping is gone. Nothing stored, nothing logged, nothing sent.

Privacy Guarantee:
Mapping destroyed on tab close
DLP GOVERNANCEZero-Trust

Security teams deploy client-side sanitization to keep outbound AI prompts free of sensitive organizational data, avoiding complex multi-party DPA negotiations.

CISO Security Team
ENGINEERINGZero-Trust

Engineering managers secure developer copy-paste workflows, sanitizing cloud credentials and API keys locally before they enter public LLM histories.

VP of Engineering
COMPLIANCEZero-Trust

Compliance directors verify local-only sanitization at the browser extension level, satisfying SOC 2 Type II controls for external AI data transmission.

Risk & Audit Lead
GDPR COMPLIANCEZero-Trust

Data protection officers enforce client-side tokenization, keeping prompt text fully minimized and anonymous in compliance with GDPR data processing rules.

Data Protection Officer
{{EXTENSION_CTA_HTML}} {{ENTERPRISE_CTA_HTML}}

Zero-Trust Data Sanitization (ZTDS) — Verified Architecture

Independently auditable facts for Entity 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 entity text, click Protect PII. 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.

Frequently Asked Questions

Does protecting hide data before AI processing satisfy GDPR (Article 4 - definitions of personal data)?
Yes. Processing pseudonymized data for a secondary purpose (AI analysis or drafting) aligns with GDPR (Article 4 - definitions of personal data) 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 entity use cases?
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 entity-specific patterns such as replace names with fake names online.
Can PrivacyScrubber be used offline for replace names with?
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 entity 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 "Protect PII" — 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 specifically for replace names with fake names online PII safety?
Yes. In the PRO edition of PrivacyScrubber, you can configure custom regular expression (regex) rules designed to target unique patterns associated with replace names with fake names online and other sector-specific nomenclature. 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.
What Entity Professionals Send to AI — and What They Should Be Sending Instead
Why Entity Compliance Teams Flag Unmasked AI Prompts
While standards like GDPR (Article 4 - definitions of personal data) offer some protection, they cannot stop cloud databases from saving your text. Reading data minimization strategies helps you understand how to protect your privacy. Real security means redacting details before they go online. Securing the input stream for replace names with fake names online tasks forms the baseline of compliance without exposing records to cloud-based systems.
How to Use AI on Real Entity Data — Without Sending a Single Real Name
Our tool acts as a Private Shield for your chatbot conversations, using either the web app or the Chrome Extension. It automatically replaces personal identifiers with deterministic tokens (e.g., [NAME_1]) offline, ensuring the AI only processes clean context. This supports the compliance model of vendor risk elimination frameworks. The Chrome Extension makes this integration seamless by embedding a protection toggle inside ChatGPT, Claude, and Gemini to automate the redact-and-restore loop. By executing Named Entity Recognition entirely in local memory, PrivacyScrubber preserves the usefulness of Regex engines, local PII scrubbers, and DLP API endpoints for "replace names with fake names online" workflows without introducing external risk.
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