feat: add PII detetction

[e06084]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @e06084, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a significant enhancement to data security by adding a new feature for detecting Personal Identifiable Information (PII) within text. The primary objective is to proactively identify and manage sensitive data like ID numbers, phone numbers, emails, and credit card details. This addition not only strengthens the system's ability to protect sensitive information but also provides mechanisms for validating and masking such data, aligning with modern data protection standards and improving overall data governance.

Highlights

• New PII Detection Rule: A new RulePIIDetection class has been introduced to identify various types of Personal Identifiable Information (PII) within text content, registered under the QUALITY_BAD_SECURITY metric.
• Comprehensive PII Patterns and Validation: The rule supports detection of a wide range of PII, including Chinese ID cards, credit cards, Chinese mobile phones, email addresses, US Social Security Numbers (SSN), Chinese passports, and IP addresses. It incorporates advanced validation logic such as the Luhn algorithm for credit cards and specific IP address validation.
• Sensitive Data Masking: Custom masking strategies have been implemented for different PII types (e.g., email, phone, ID card, credit card, IP address) to redact sensitive information while retaining partial visibility for context.
• Updated Documentation and Extensive Testing: The metrics.md documentation has been updated to reflect the new PII detection capability, and comprehensive unit tests have been added to ensure the accuracy and reliability of the PII detection, validation, and masking functionalities.

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[gemini-code-assist]

Code Review

This pull request introduces a new rule for detecting Personally Identifiable Information (PII), which is a great addition for improving data security. The implementation is well-structured with regex patterns, validators, and masking logic. The test coverage is also quite comprehensive.

My review includes a few suggestions to improve the correctness and maintainability of the new rule:

• A high-severity fix for the credit card detection regex to support all valid card lengths.
• A correction for the email detection regex.
• Suggestions for refactoring the PII masking and result grouping logic for better efficiency and maintainability.
• A recommendation to expand test coverage for credit card numbers to include more lengths.

Overall, this is a solid contribution. Addressing these points will make the PII detection feature more robust and reliable.

[gemini-code-assist]

These three list comprehensions iterate over detected_pii three separate times. For better performance, you could iterate once and group the items by severity. Using collections.defaultdict would be an efficient way to achieve this.

Example:

from collections import defaultdict

pii_by_severity = defaultdict(list)
for item in detected_pii:
    pii_by_severity[item["severity"]].append(item)

high_severity = pii_by_severity.get("high", [])
# ... and so on for other severities

This approach is more efficient and scales better if more severity levels are added in the future.