The Enigma Vault blog
Practical guides to data privacy and grounded AI.
Research, technical deep dives, product updates, and enterprise guidance on AI security, privacy, and trusted data.
All articles
9 articles
Best PCI DSS Compliant Tokenization Software (2026): 10 Platforms Compared
Ten PCI DSS compliant tokenization platforms compared on Level 1 status, scope reduction, SAQ impact, ratings and pricing.
Best Credit Card Tokenization Software (2026): 9 Platforms Compared
Nine credit card tokenization platforms compared on token portability, capture channels, token format, PCI evidence and pricing, plus where your gateway's native vault fits.
LLM Data Leakage: Where PII Ends Up After You Send a Prompt
LLM data leakage starts when PII enters a prompt. Learn where those copies end up and why trusting your LLM provider alone won't stop them spreading.
Best Data Tokenization Software (2026): 10 Tools Compared
I compared ten data tokenization platforms on token model, compliance, public pricing and verified review scores, then matched each one to the buyer it suits.
Best Payment Tokenization Software (2026): 9 Tools Compared
Nine payment tokenization platforms compared on architecture, processor portability, public pricing and verified review scores, with a guide to choosing by scenario.
Data Tokenization: Use Cases and How to Choose the Right Solution
Data tokenization replaces sensitive values such as SSNs, bank account numbers and health details with random tokens, while the real data stays encrypted inside a secure vault. Here is how it works and how to choose a solution.
What Is Vault vs Vaultless Tokenization? Key Differences Explained
Vault-based tokenization stores originals in an encrypted vault and maps them to random tokens, while vaultless tokenization derives tokens with a key and stores nothing. Here is how they differ and how to choose.
OpenAI Privacy Filter Is Great. But here's why PII Redaction Breaks Multi-Turn LLM Workflows
OpenAI's Privacy Filter is a strong open-weight PII detection model, but it redacts irreversibly, covers eight categories and is not a proxy. Production LLM apps need reversible tokenization, persistent entity mapping across turns and multi-provider coverage.
Tokenization vs. Masking vs. Redaction: Which Protects AI Prompts Best?
Compare tokenization, masking, and redaction for AI prompt protection. See why deterministic tokenization preserves LLM reasoning while masking and redaction break it.
No articles match that filter.