privacylens 0.1.2

Transparent PII masking for LLM clients — keep sensitive data out of your AI prompts

PrivacyLens

Transparent PII masking for LLM clients — keep sensitive data out of your AI prompts.

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Why?

Every prompt you send to an LLM can leak PII — names, emails, phone numbers, SSNs. PrivacyLens intercepts your prompts, replaces PII with anonymous tokens, and restores the original values when the response comes back. Your LLM never sees real data.

Input:  "Email [e-mail] about the project"
Sent:   "Email [EMAIL_1] about the project"         ← LLM sees this
Output: "I've emailed [e-mail]"              ← Your app sees this

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Install

pip install privacylens

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Usage

Step 1: Wrap your client

from privacylens import shield

# Pick your LLM client — wrap it with shield()
import openai
client = shield(openai.OpenAI())

Step 2: Use it normally

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": "My name is John Doe and my email is [e-mail]. Write me a welcome email."
    }],
)

print(response.choices[0].message.content)
# Output contains "John Doe" and "[e-mail]" — restored automatically

That's it. No other code changes needed. The PII is masked before it reaches the LLM and unmasked in the response.

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Works With Every Major LLM Client

from privacylens import shield

# OpenAI
client = shield(openai.OpenAI())
client = shield(openai.AsyncOpenAI())

# Anthropic
client = shield(anthropic.Anthropic())
client = shield(anthropic.AsyncAnthropic())

# LangChain — returns a callback handler
handler = shield(my_langchain_chat_model)

# CrewAI
adapter = shield(my_crewai_agent)

# Strands
wrapper = shield(my_strands_model)

Each wrapped client behaves exactly like the original. Same methods, same parameters, same return types.

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What Gets Detected

Built-in (regex, zero dependencies)

Entity	Example Input	What LLM Sees
Email	[e-mail]	[EMAIL_1]
Phone	555-123-4567	[PHONE_1]
SSN	123-45-6789	[SSN_1]

Optional: Presidio (50+ entity types)

pip install privacylens[pii]

Detects names, addresses, credit card numbers, dates of birth, passport numbers, and more using Microsoft Presidio.

Optional: GLiNER (ML-based semantic detection)

pip install privacylens[semantic]

Uses a neural model to detect entities that regex can't catch.

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Custom Detectors

Add your own patterns via privacylens.yaml in your project root:

detectors:
  regex:
    patterns:
      - entity_type: EMAIL
        pattern: '[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}'
      - entity_type: EMPLOYEE_ID
        pattern: 'EMP-\d{5,}'
      - entity_type: PROJECT_CODE
        pattern: 'PROJ-[A-Z]{2,4}-\d{3,}'

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Vault Backends

Tokens are stored in a vault so they can be restored later. Three backends available:

# In-memory (default) — fast, lost on restart
vault: memory

# SQLite — persists to disk
vault: sqlite

# Redis — shared across processes
vault: redis

For Redis:

pip install privacylens[redis]

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Inspect Without Masking

See what would be detected without actually masking anything:

from privacylens import inspect

entities = inspect("Contact [e-mail] or call 555-123-4567")

for entity in entities:
    print(f"{entity.entity_type}: '{entity.value}' at [{entity.start}:{entity.end}]")

# EMAIL: '[e-mail]' at [8:24]
# PHONE: '555-123-4567' at [33:45]

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Low-Level API

For full control over the pipeline:

from privacylens.core.pipeline import Pipeline
from privacylens.core.config import load_config

config = load_config()
pipeline = Pipeline(config)

# Tokenize
messages = [{"role": "user", "content": "Email [e-mail]"}]
tokenized = pipeline.tokenize_messages(messages, session_id="s1")

# Send to LLM (tokenized messages have PII replaced)
llm_response = call_your_llm(tokenized)

# Detokenize
restored = pipeline.detokenize(llm_response, session_id="s1")

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Links

• GitHub: github.com/Madan2248c/privacylens
• TypeScript SDK: npm install privacylens — docs
• Contributing: CONTRIBUTING.md

License

MIT © 2026 Madan Gopal