Stop PII and IP from leaking into AI — before the model sees it.
Detection logs a leak. Prevention stops it. ShadowIQ redacts, tokenizes, or denies sensitive content inline, before a request ever leaves your perimeter.
Summary
ShadowIQ prevents AI data leakage by detecting PII, PCI, PHI, and customer-schema identifiers inline at the AI gateway, applying redaction, tokenization, or denial actions in under 75ms with cryptographically signed evidence of every decision.
The before / after, in one picture.
You've heard this one before.
- Employees pasting SSNs and customer data into ChatGPT.
- Legacy DLP that doesn't understand prompt context.
- PII redaction that mangles the response quality.
- No record of what was almost leaked.
Three moves.
- 1Context-aware detection.
SSN, passport, PAN, PHI, and customer-schema identifiers — detected with context (prompt, retrieval, tool-use), not brittle regex.
- 2Redact, tokenize, or deny.
Configurable per-policy actions. Tokenization preserves answer quality; redaction is deterministic and reversible on policy approval.
- 3Signed 'almost-leaked' record.
Every detection — whether acted on or not — is signed and queryable. Auditors can confirm your DLP worked; attackers can't claim it didn't.
Numbers, not adjectives.
Asked, answered, sourced.
Yes. We integrate with Microsoft Purview, Symantec, and Forcepoint DLP via classification signals and policy sync. ShadowIQ adds the AI context those tools can't see.
Tokenization preserves semantic structure so the model can reason over placeholders and produce a useful answer. Deterministic tokens round-trip cleanly on approved policies.
Upload a schema (table.column with regex or enum) and we build a detector. Customer-schema detectors are versioned and signed like policies.
Keep going.
Your 30-minute demo. A signed audit trail by the end of it.
We'll wire ShadowIQ into one live workload, block a prompt injection in real time, and hand you a cryptographic receipt — before the meeting ends.