---

Abstract

Data engineers and ML practitioners routinely work with datasets that silently contain Personally Identifiable Information (PII) — emails, phone numbers, SSNs, credit card numbers, and IP addresses — creating compliance risks under GDPR, CCPA, and HIPAA. pii-radar is a lightweight, zero-dependency-ML CLI tool that scans structured data files for PII using high-precision patterns, Luhn Mod-10 verification, and contextual heuristics, outputting results as rich terminal tables, JSON, or CSV reports. It integrates natively with pre-commit hooks and GitHub Actions to catch PII before it reaches production or version control.

☁️ Azure Cloud Integration

pii-radar provides streaming PII redaction components for Microsoft Azure Storage and Azure Event Hubs:

Flow 1: Stream and Redact Files in Azure Blob Storage

from pii_radar.integrations import AzureBlobStreamRedactor

# Scans CSV/JSON blobs in Azure Blob Storage and uploads redacted sanitized copies
redactor = AzureBlobStreamRedactor(
    connection_string="DefaultEndpointsProtocol=https;...",
    container_name="customer-data"
)
total_found, counts = redactor.redact_blob("raw_customers.csv", output_blob_name="sanitized_customers.csv")
print(f"Redacted {total_found} PII occurrences in Azure Blob Storage.")

Flow 2: Real-Time PII Redaction in Azure Event Hubs

from pii_radar.integrations import AzureEventHubHandler

# Redacts sensitive PII in real-time telemetry streaming event batches
handler = AzureEventHubHandler(
    connection_string="Endpoint=sb://...",
    eventhub_name="telemetry-hub"
)
redacted_events = handler.process_event_batch(raw_event_messages)

---

🚀 Usage Guides

• ⚡ Azure Blob Storage Stream Redactor — Real-time PII scanning & masking for CSV/JSON files in Azure Storage containers (AzureBlobStreamRedactor)
• 📡 Azure Event Hubs Integration — Low-latency PII redaction pipeline for streaming telemetry in Azure Event Hubs (AzureEventHubHandler)
• 📁 3 file formats — CSV, JSON, Parquet (.parquet, .pq)
• 📂 Folder scanning — Recursively scan entire directories
• 🎨 Beautiful terminal output — Rich tables with confidence scores
• 🤖 CI/CD native — --fail-on-detect exits with code 1 for pipeline gates
• ⚡ Row sampling — --sample 1000 limit for rapid audit sampling on massive files
• 🔒 Auto-redaction — --redact creates a sanitized copy of your data
• 📊 CSV reports — Save all findings to a structured report file
• ⚡ Fast — Pure regex + algorithmic validation, no heavy ML models

---

📦 Installation

# Base installation (Lightweight)
pip install pii-radar

# With Azure Blob Storage & Azure Event Hubs support
pip install "pii-radar[azure]"

# With Parquet support
pip install "pii-radar[parquet]"

# Everything (Azure Blob/EventHubs + Parquet)
pip install "pii-radar[all]"

Or install from source:

git clone https://github.com/nithin42/pii-radar.git
cd pii-radar
pip install -e ".[dev]"

---

🚀 Quick Start

# Scan a CSV file
pii-radar scan data/customers.csv

# Fast sampling (scan only first 1,000 rows)
pii-radar scan data/large_file.csv --sample 1000

# Scan a JSON file
pii-radar scan logs/events.json

# Scan an entire directory
pii-radar scan data/

# Get JSON output (great for scripts)
pii-radar scan data.csv --output json

# Only show high-confidence detections
pii-radar scan data.csv --min-confidence 0.9

# Save a report to CSV
pii-radar scan data.csv --report pii_report.csv

# Create a redacted copy
pii-radar scan data.csv --redact data_clean.csv

# Use in CI/CD — fails build if PII found
pii-radar scan data.csv --fail-on-detect

---

🏗️ Architecture

CLI Interface (cli.py)
   │
   ├─► scan_file / scan_directory (scanner.py)
   │     │
   │     ├─► File Readers (readers.py) — CSV / JSON / Parquet Cell Stream
   │     │
   │     └─► Heuristic Engine (detectors.py)
   │           ├─ Email (RFC-compliant regex)
   │           ├─ SSN (Format + Range Rejection)
   │           ├─ Credit Card (Luhn Mod-10 Checksum)
   │           ├─ Phone (Word-bounded pattern)
   │           ├─ IP Address (IPv4 0-255 Octet Validation)
   │           └─ Date of Birth (Column-Name Heuristic + Format)
   │
   └─► Reporting Layer (reporter.py)
         ├─ Rich Terminal Panel & Table
         ├─ JSON Pipeline Stream
         └─ CSV Compliance Report

---

📊 Detection Capabilities & Validation

PII Type	Verification Strategy	Accuracy / False Positive Defense
EMAIL	RFC-compliant regex	99% — Word boundary enforced
SSN	Format + Area exclusion	98% — Rejects invalid 000, 666, 900+ ranges
CREDIT_CARD	Luhn Mod-10 Algorithm	99% — Eliminates random 16-digit number false positives
IP_ADDRESS	IPv4 + Octet range check	95% — Rejects 999.x.x.x and version strings
PHONE	US/International regex	92% — Enforces strict \b word boundaries
DATE_OF_BIRTH	Format + Column Heuristics	95% — Contextual matching (dob, birth, bday)

---

🧪 Performance Benchmark

Run the reproducible benchmark script locally:

python examples/benchmark.py

• Dataset: 10,000 rows x 7 columns (70,000 cells)
• Throughput: ~45,000–60,000 cells/second
• Memory Overhead: Minimal (generator-based cell streaming)

---

🔧 CI/CD Integration

GitHub Actions

- name: Scan for PII before merge
  run: |
    pip install pii-radar
    pii-radar scan data/ --fail-on-detect --min-confidence 0.85

Pre-commit Hook

Add to .pre-commit-config.yaml:

- repo: local
  hooks:
    - id: pii-radar
      name: PII Scanner
      entry: pii-radar scan
      args: [--fail-on-detect, --min-confidence, "0.9"]
      language: python
      types: [csv, json]

---

📁 Project Structure

pii-radar/
├── src/pii_radar/
│   ├── cli.py          ← Click CLI entry point (--sample, --fail-on-detect)
│   ├── scanner.py      ← Core scan orchestration with row limits
│   ├── detectors.py    ← Luhn + IPv4 range + DOB heuristics engine
│   ├── readers.py      ← CSV / JSON / Parquet readers
│   └── reporter.py     ← Rich terminal + JSON + CSV output
├── tests/
│   ├── conftest.py     ← Shared fixtures
│   ├── test_detectors.py
│   ├── test_negative_cases.py  ← False positive & Luhn unit tests
│   ├── test_scanner.py
│   └── test_cli.py
├── examples/
│   ├── sample.csv
│   ├── sample.json
│   └── benchmark.py    ← Performance benchmarking tool
├── .github/workflows/  ← CI/CD matrix (Ubuntu + Windows)
├── pyproject.toml
├── Makefile
└── README.md

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📄 License

MIT — see LICENSE.

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👤 Author

Nithin · github.com/nithin42 · [email protected]

Part of an elite Data Science & Secure Computing portfolio. Focused on data privacy, reproducible ML, and secure systems engineering.