fix(masking): validate unmatched columns and support snake_case matching

[Voyagerroc-Lab]

Summary of Changes

This PR addresses #251 by handling unmatched and variant column names in masking rules:

• Snake-case / Case-normalization Column Matching:
  • _resolve_columns now matches rule columns against DataFrame columns using both exact names and snake_case normalization (snake_case(col)), ensuring common casing variants (like --mask Email:hash for email, or First Name for first_name) resolve correctly.
• Strict Validation for Missing Columns:
  • Added strict: bool = True field to MaskingRule and strict: bool | None = None parameter to mask_dataframe.
  • When explicit rule.columns are provided and any are missing from the DataFrame, ValueError is raised in strict mode (the default), causing the CLI to exit with code 1 and a descriptive error message instead of silently exiting 0 with unmasked data.
• Audit Logging of Unmatched Columns:
  • Added unmatched_columns: list[str] to MaskReport and included it in MaskReport.to_dict(), capturing any unmatched columns for compliance and auditing when running in non-strict mode.
• Testing:
  • Added unit tests in tests/test_enterprise_cleaner.py for snake_case column matching, strict ValueError raising on missing columns, and non-strict tracking in MaskReport.unmatched_columns.
  • Added CLI tests in tests/test_enterprise_cli.py verifying --mask Email:hash successfully masks email and --mask non_existent:hash exits with code 1 and an informative error message.

Closes #251

[coderabbitai]

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