Workflow
Anonymize Text Before AI
A practical guide to protecting sensitive professional text locally before using ChatGPT or other AI tools, with reversible protection and local restoration.
Resources
Guides
Practical guides for protecting sensitive information, understanding privacy techniques, and using AI more safely.
AI & Sensitive Data
Practical guidance for protecting sensitive professional information when working with AI.
Risk & Prevention
AI Data Leakage
Learn how sensitive information can leak through AI prompts, uploads, shadow AI, and weak controls, plus practical prevention layers.
Provider Privacy
ChatGPT Privacy
Learn how ChatGPT handles conversations, training, retention, privacy controls, managed accounts, and sensitive information.
Tokenization, Data Masking & PII
Learn how sensitive values can be tokenized, masked, redacted, or protected across structured data and unstructured text.
Tokenization
Data Tokenization
Learn how data tokenization replaces sensitive values with reversible tokens across structured systems and unstructured professional text.
Comparison
Data Masking vs Tokenization
Compare how masking and tokenization differ across reversibility, storage, databases, test data, unstructured text, and protected AI workflows.
Comparison
Data Masking vs Data Redaction
Understand when masking preserves useful structure, when redaction should remove information, and where reversible tokenization fits.
PII Protection
PII Masking
Learn how masking protects personal information across databases, logs, APIs, documents, and AI prompts through different techniques.
Data Anonymization
Explore the difference between anonymization and reversible protection, plus the techniques used to reduce identifiability.
Comparison
Data Masking vs Anonymization
Compare masking and anonymization through reversibility, pseudonymization, re-identification risk, structured data, and AI workflows.
Methods & Techniques
Data Anonymization Techniques
Explore common anonymization methods, their uses and limits, and how they differ across structured data, free text, and AI workflows.