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Terms of Service | Privacy Policy | PII Methodology

PRIVACY POLICY FOR SHEET ARMY KNIFELast updated: 7/23/2025WHAT DATA WE COLLECT:
- None. All file processing happens locally in your browser.HOW WE USE YOUR DATA:
- Your files never leave your device
- No data is sent to our servers
- License validation sends only license keys to LemonSqueezy (our payment processor)DATA STORAGE:
- Files are processed temporarily in browser memory only
- License information stored locally in Chrome sync storage
- No permanent data storage on external serversTHIRD PARTY SERVICES:
- LemonSqueezy: For license validation only (license keys)
- No analytics, tracking, or advertising servicesCONTACT:
Please reach out via the Contact Us section on the homepage.Your privacy is important to us. This extension is designed to keep your data private and secure.

PII Detection MethodologyLast Updated: 2025/08/03
Version: 1.0OverviewSheet Army Knife uses automated pattern recognition to help identify common types of Personally Identifiable Information (PII) in spreadsheet data. This document explains our detection methodology, capabilities, and limitations to help you make informed decisions about your data.Important: This tool provides assistance in identifying potential PII but should not replace human review and judgment. You remain responsible for all decisions regarding data sharing and privacy protection.What We DetectOur system scans for the following types of PII using pattern matching and contextual analysis:1. Email Addresses
• Patterns: Standard email formats ([email protected])
• Examples: [email protected], [email protected]
• Context: Any column or cell containing email-like patterns
• Limitations: May not detect all international email formats2. Phone Numbers
• Patterns: US phone number formats with various separators
• Examples: (555) 123-4567, 555-123-4567, 555.123.4567, 5551234567, +1 555 123 4567, 555-123-4567 ext 123
• Context: Any column or cell containing phone-like patterns
• Limitations: US formats only; international numbers not supported3. Social Security Numbers
• Patterns: 9-digit numbers in XXX-XX-XXXX format or similar
• Examples: 123-45-6789, 123 45 6789, 123.45.6789, 123456789
• Context: Any column or cell containing SSN-like patterns
• Limitations: Filters out obvious test numbers (111-11-1111, etc.)4. Credit Card Numbers
• Patterns: 14-16 digit payment card numbers
• Examples: 4532 1234 5678 9012 (Visa/Mastercard - 16 digits), 3782 822463 10005 (American Express - 15 digits), 3056 930009 0004 (Diners Club - 14 digits)
• Context: Any column or cell containing card-like number patterns
• Limitations: Pattern-based detection only; does not validate actual card numbers5. Street Addresses
• Patterns: Number + street name + street type format
• Examples: 123 Main Street, 456 Oak Ave, 789 Corporate Blvd
• Context: Any column or cell containing address-like patterns
• Limitations: US street formats only; requires house number and standard street types6. ZIP Codes
• Patterns: 5-digit or 5+4 digit postal codes
• Examples: 12345, 90210-1234, 12345 6789
• Context: Only flagged when appearing in postal-related contexts (columns named "zip", "postal", "zipcode", etc.)
• Limitations: US ZIP codes only; requires contextual indicators7. IP Addresses
• Patterns: IPv4 addresses with valid ranges (0-255 per octet)
• Examples: 192.168.1.1, 10.0.0.1, 127.0.0.1
• Context: Only flagged when appearing in network-related contexts (columns named "ip", "server", "host", etc.)
• Limitations: IPv4 only; requires contextual indicators8. Dates of Birth
• Patterns: MM/DD/YYYY format dates from 1900-2099
• Examples: 12/25/1990, 01-15-1985, 3.5.2000
• Context: Only flagged when appearing in birth-related contexts (columns named "dob", "birthday", "birth", etc.)
• Limitations: US date format only; requires contextual indicators9. Bank Account Numbers
• Patterns: 8-17 digit numbers, with optional separators
• Examples: 123456789012, 123-456-789-012, 123 456 789 012
• Context: Only flagged when appearing in banking-related contexts (columns named "bank", "routing", "checking", etc.)
• Limitations: Requires banking context; filters out obvious test numbers10. Driver License Numbers
• Patterns: Letter prefix + numbers OR all-numeric formats
• Examples: A1234567 (California-style), 123456789 (Texas-style), CA123456789 (extended formats)
• Context: Only flagged when appearing in license-related contexts (columns named "license", "driver", "dl", etc.)
• Limitations: Common US formats only; requires contextual indicators11. Personal Names
Patterns: Properly capitalized word sequences (1-4 words, 2-20 characters each)
Examples: John Smith, Mary Johnson, David Lee, Sarah Williams-Brown
Context: Only flagged when appearing in name-related contexts (columns named "name", "first", "last", "customer", "contact", etc.) OR when a high percentage of entries match common first names
Detection Method: Uses pattern matching combined with:Common first name recognition (1,000+ popular names)
Proper capitalization validation
Contextual header analysis
Statistical filtering to reduce false positivesLimitations:Primarily optimized for English names with standard capitalization
May miss names with unusual formatting, international characters, or non-English origins
Requires either clear contextual indicators or high concentration of recognizable first names
Conservative approach may miss some valid names to avoid false positives
Does not detect names in all-caps, all-lowercase, or mixed with titles/prefixesHow Detection WorksPattern Matching
We use regular expressions (regex) to identify data that matches common PII formats. Each pattern is designed to balance detection accuracy with false positive prevention.Contextual Analysis
For certain PII types (ZIP codes, IP addresses, dates of birth, bank accounts, driver licenses), we only flag data when it appears in contextually appropriate locations, such as:
• Column headers containing relevant keywords
• Cell data containing contextual indicatorsValidation Filtering
We filter out obvious test or fake data patterns, such as:
• Sequential numbers (123456789)
• Repetitive patterns (111111111)
• Common test values (000000000)What We Don't DetectInternational Formats
• Non-US phone numbers
• International postal codes
• Non-US date formats (DD/MM/YYYY)
• International ID numbersComplex Variations
• Unusual formatting or separators
• Abbreviated or coded references
• Encrypted or hashed PII
• PII embedded in longer text stringsContext-Dependent Information
• Names (without clear indicators)
• Ages or birth years alone
• Partial address information
• Business vs. personal distinctions• Names with unusual capitalization, titles, or international characters
• Names in all-caps or all-lowercase formats
• Names with extensive use of hyphens, apostrophes, or special charactersLimitations and DisclaimersFalse Positives
Our system may occasionally flag non-PII data that matches common patterns, such as:
• Product model numbers resembling phone numbers
• Version numbers resembling IP addresses
• Random number sequences matching PII patterns
• Capitalized words that aren't names (like "Project Alpha", "Team Beta")
• Product names or titles that follow name-like patternsFalse Negatives
Our system may miss PII in the following situations:
• Unusual or non-standard formatting
• International formats not supported
• Data lacking contextual indicators
• Encrypted or encoded informationNo Guarantee of Complete Detection
This tool provides assistance in identifying potential PII but cannot guarantee:
• Detection of all sensitive information
• Accuracy in all situations
• Compliance with specific privacy regulations
• Suitability for all use casesYour ResponsibilitiesManual Review Required
You should always:
• Review all data before sharing with AI tools
• Verify detection results for accuracy
• Look for PII types not covered by our patterns
• Make final decisions about data sensitivityPrivacy Compliance
You remain responsible for:
• Compliance with applicable privacy laws (GDPR, CCPA, etc.)
• Understanding your organization's data policies
• Making appropriate data handling decisions
• Protecting sensitive informationData Security
You should:
• Use anonymization features when appropriate
• Keep mapping files secure if using de-anonymization
• Follow your organization's data security policies
• Consider additional privacy measures as neededUpdates and ChangesThis methodology may be updated periodically to:
• Improve detection accuracy
• Add support for new PII types
• Reduce false positives
• Address user feedbackSignificant changes will be communicated through:
• Updated version numbers and dates
• Extension notifications (where appropriate)
• Website announcementsContact and SupportIf you have questions about our PII detection methodology or need assistance, please reach out via the Contact Us section on the homepage.---Disclaimer: This document describes the current capabilities of Sheet Army Knife's PII detection system. The tool is provided "as-is" without warranty of any kind. Users assume all responsibility for data protection decisions and should not rely solely on automated detection for sensitive data handling.