fastpii 0.2.4.1

Privacy infrastructure for AI applications handling Czech and European data

FastPII

Privacy infrastructure for AI applications handling Czech and European data

FastPII detects, validates, anonymizes, and protects sensitive data before it reaches LLMs, RAG systems, vector databases, AI agents, or third-party AI providers.

Built for AI-native applications. Designed for privacy-first architectures.

Quick Start · Privacy Modes · AI Use Cases · Benchmarks · Documentation

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Why FastPII

Most PII tools are built for generic text processing. FastPII is built for AI workflows.

Modern applications increasingly send documents, prompts, support tickets, contracts, medical records, and business data directly into LLMs and AI systems. FastPII acts as the privacy layer between your data and your AI.

Without FastPII          With FastPII

Document                 Document
  ↓                        ↓
LLM                      FastPII
                           ↓
                         LLM

Sensitive data exposed   Sensitive data protected

Czech-Native Detection

Unlike generic PII tools, FastPII understands Czech identifiers and validation rules:

• Rodné číslo · IČO · DIČ · Bank Accounts
• Phone Numbers · Postal Codes (PSČ) · Addresses
• Personal Names · Vehicle Plates · Dates of Birth · Email Addresses

Checksum Validation

FastPII validates identifiers instead of relying solely on pattern matching. Rodné číslo (Mod 11), IČO (weighted Mod 11), Czech bank accounts, and DIČ formats all require valid checksums — structurally invalid identifiers are rejected before classification.

Context-Aware Detection

Detection is not based on regex alone. Phone numbers require context words or a +420 prefix. Postal codes require address proximity. Dates of birth need birth-related keywords nearby. Addresses use component scoring. This significantly reduces false positives.

Built for AI Workflows

FastPII integrates directly into RAG pipelines, LangChain applications, MCP servers, AI agents, FastAPI applications, and enterprise AI systems.

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Features

Detection	Identify sensitive Czech and European data
Validation	Validate identifiers using official checksum rules
Privacy Protection	Four modes: anonymize, redact, mask, remove
Framework-Independent SDK	Use as a standalone Python package
Integrations	FastAPI, LangChain, MCP, CLI
Local First	No cloud, no LLM, no external API calls required

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Installation

pip install fastpii

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Quick Start

from fastpii import PrivacyGuard

guard = PrivacyGuard(regions=["cz"])

text = "Jan Novák, RČ: 800101/1238, IČO: 25596641"
result = guard.detect(text)

for finding in result.findings:
    print(f"{finding.type}: {finding.value}")
    # name: Jan Novák
    # rodne_cislo: 8001011238
    # ico: 25596641

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Privacy Modes

Anonymize — Replace with [REDACTED]

guard.anonymize("Jan Novák, RČ: 800101/1238")
# → "[REDACTED], RČ: [REDACTED]"

Redact — Replace with PII type label

guard.redact("Jan Novák, RČ: 800101/1238")
# → "[NAME], RČ: [RODNE_CISLO]"

Mask — Replace with asterisks

guard.mask("Jan Novák")
# → "*********"

Remove — Delete PII entirely

guard.remove("Jan Novák")
# → ""

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AI Use Cases

Protect RAG Pipelines

safe_document = guard.anonymize(document)
embeddings = embed_model.embed(safe_document)

Protect LLM Prompts

safe_prompt = guard.anonymize(prompt)
response = llm.invoke(safe_prompt)

Protect MCP Tools

safe_input = guard.anonymize(user_input)
result = tool.execute(safe_input)

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Supported Czech Entities

Entity	Detection Method	Checksum
Rodné číslo	Mod 11 checksum + date validation	✓
IČO	Weighted Mod 11 checksum	✓
DIČ	Multi-format + IČO validation	✓
Bank Account	Two-part Mod 11 checksum	✓
Postal Code	Context-gated (PSČ label, city, address proximity)	—
Phone Number	Context-gated (+420 prefix or context words)	—
Date of Birth	Context-gated (birth keywords, intervening date blocking)	—
Address	Component scoring (street + number + city + postal)	—
Name	Czech name dictionary + gender classification	—
Email	Czech TLD detection, markdown mailto handling	—
Vehicle Plate	Regional code validation	—

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Benchmarks

Evaluated on Czech-focused datasets containing contracts, medical records, business registries, support tickets, and adversarial false-positive scenarios.

v0.2.4 overall:

Metric	Score
Precision	84.2%
Recall	80.0%
F1	82.1%

Per-detector:

Detector	Precision	Recall	Notes
IČO	100%	100%	Checksum-validated, no FPs
DIČ	100%	100%	Multi-format detection
Email	100%	100%	Markdown mailto handled
Date	100%	100%	Non-birth dates detected separately
Phone	100%	100%	Context or +420 prefix required
Vehicle Plate	100%	100%	Regional code validation
Date of Birth	100%	86%	Context-gated; rejects generic dates
Postal Code	100%	71%	Subsumed by address in overlaps
Name	80%	100%	Dict-matched; corporate name FPs
Address	71%	63%	Component scoring; partial matches
Rodné číslo	67%	50%	Invalid checksums correctly rejected
Bank Account	100%	0%	Requires labeled context (v0.2.5)

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Roadmap

Current — Core SDK, Czech Detectors, Validation Engine, CLI, FastAPI Integration, LangChain Integration

Next (v0.2.5) — Strict Mode, MCP Integration, RAG Middleware, Improved Address & Bank Account Detection

Future — FastPII Gateway, Policy Engine, Audit Logging, Enterprise Features, Additional European Regions

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Documentation

• Quick Start
• API Reference
• Detector Documentation
• Integration Guides
• fastpii.com

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Contributing

Contributions welcome! See Contributing Guide.

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License

Apache 2.0 — See LICENSE for details.

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Built for privacy-first AI applications