VeilPhantom

VeilPhantom is a Data & Analytics tool that provides privacy-preserving PII redaction for AI pipelines. It's an open-source Python SDK that detects and redacts sensitive data before it reaches any LLM.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is VeilPhantom?

VeilPhantom is an open-source Python SDK designed for robust PII detection and redaction within AI pipelines. It ensures zero PII leakage by processing sensitive data on-device, preventing it from ever reaching large language models. The SDK features a 7-layer detection pipeline capable of identifying 19 distinct PII entity types, including cross-cultural names, emails, phone numbers, and financial data. It offers a token-direct pipeline that replaces sensitive values with trackable tokens like [PERSON_1], preserving structural context for the AI while maintaining privacy. Full rehydration capabilities allow original values to be restored in AI responses, and it integrates seamlessly with OpenAI, LangChain, and other LLMs, providing a drop-in privacy solution for existing AI workflows.

Best used for

Ideal for developers and data scientists who need to protect sensitive data in AI pipelines, ensure PII never reaches LLMs, and maintain compliance. Especially valuable for integrating privacy into existing OpenAI or LangChain applications with minimal overhead, while preserving AI model accuracy.

Common actions

redact PII

detect sensitive data

ensure data privacy

integrate PII protection

anonymize text

Capabilities

Key features

7-layer PII detection
19 PII token types
Zero PII leakage
Token-direct pipeline
Full rehydration
LLM integrations
Cross-cultural name detection

Target Audience

developerdata scientist

Integrations

openailangchain

Pricing & Plans

Open Source

FAQs

What is the core privacy mechanism of VeilPhantom?

VeilPhantom uses a 7-layer detection pipeline to identify 19 types of PII. It then replaces this sensitive data with trackable tokens (e.g., [PERSON_1]) before sending it to an LLM. This token-direct approach ensures real data never leaves your machine, and original values can be rehydrated locally.

Does VeilPhantom impact the accuracy of LLM responses?

Benchmarking shows VeilPhantom can actually improve tool accuracy by +1.9% with Claude Haiku 4.5. The token-structured input helps the LLM parse arguments more reliably, and the redaction overhead is minimal, averaging only 6ms per scenario.

Can VeilPhantom detect PII in various languages or cultural contexts?

Yes, VeilPhantom's Shade V7 NER model is designed to detect cross-cultural name patterns, including Western, African, Asian, Arabic, and South African naming conventions. It can even handle ASR-mangled transcriptions, ensuring broad coverage for diverse data.

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