AI Data Protection
That Never Forgets
Tokenize sensitive data before it ever reaches a model — reversible, format-preserving, and audit-logged end to end.
Same shape. Zero exposure.
Format-preserving encryption keeps every field the length and type it started as — a card number still looks like a card number.
This is a preview. The real engine runs in redzohu's protection layer, in front of every model call you make.
Everything your AI stack needs to stay safe and compliant
One layer between your app and every model — tokenizing sensitive data before it ever leaves your perimeter.
Tokenization
Replace sensitive fields with reversible tokens before they ever reach a model, so raw PII never leaves your perimeter.
AI Data Protection Layer
A policy layer sits between your app and every model call, redacting, masking, and auditing in real time.
Vaultless Key Custody
Encryption keys live in an external vault, never inside your database — a full database dump reveals nothing usable.
Secure Share Links
Share a tokenized value through a link and a separate one-time token — either one leaking alone never reveals the real data.
Audit Logs & Compliance
Every tokenize and detokenize call is written to a hash-chained, tamper-evident log, exportable for SOC 2 and internal audits.
One-Line SDK
Drop in one SDK call and every existing model request is tokenized and audit-logged automatically.
Quantum-Resistant by Default
Every token is built on AES-256. Grover's algorithm — the best known quantum attack on symmetric ciphers — only halves its margin, leaving a full 128-bit security floor intact. No separate post-quantum migration required.
Why teams tokenize instead of just masking
Masking and redaction make data safe by making it less useful. Tokenization doesn't have to trade one for the other.
Keeps your models smart, not blind
Masking and redaction strip a field down until it's safe — and less useful for prediction. Tokenization keeps the underlying structure and relationships intact, so models keep learning from real patterns without ever seeing the real values.
Drops into what you've already built
A token looks and behaves exactly like the field it replaces, so it moves through existing schemas, pipelines, and models with no rework — even across a full cloud migration or platform rebuild.
Protected from the first byte in
Tokenize at the point of ingestion, and sensitive data is never exposed to anything downstream — every stage of the pipeline works with protected values, without losing what makes the data useful.
Ready to protect your AI data without slowing your team down?
Get started free — no credit card required.
Get Started