Automatically detect, redact, and anonymize personally identifiable information across documents, text, and structured data. K-anonymity, differential privacy, and pseudonymization that meets GDPR, HIPAA, and CCPA requirements — while preserving the analytical utility your teams depend on.
Organizations sit on vast quantities of data they cannot use, share, or analyze because personally identifiable information is embedded throughout. Manual redaction does not scale. Generic pattern matching misses context. And regulatory penalties for getting it wrong are measured in percentages of global revenue.
Hospitals and research institutions hold patient records, clinical notes, and genomic data that could advance medical research — but HIPAA requires de-identification before any data can be shared, published, or used in secondary analysis. Manual de-identification is slow, inconsistent, and does not scale to the volume of electronic health records modern institutions generate. Automated PII detection and anonymization unlocks the analytical value of healthcare data while maintaining regulatory compliance.
Banks, insurers, and investment firms process transaction narratives, KYC documents, internal communications, and counterparty records containing names, account numbers, tax identifiers, and financial details. GDPR, CCPA, and sector-specific regulations require that PII be minimized, access-controlled, and erasable on request. The volume of data in modern financial operations makes manual compliance physically impossible.
Law firms and corporate legal teams review contracts, litigation documents, and due-diligence packages where personally identifiable information is scattered across clauses, exhibits, and correspondence. Before documents can be stored in shared repositories, used for model training, or produced in discovery, PII must be identified and handled consistently across thousands of pages per matter.
Data science teams need access to production datasets for model training, customer segmentation, and trend analysis. But production data contains PII that cannot be exposed in development environments, shared with external partners, or used in analytics platforms without transformation. The gap between “data we have” and “data we can use” is a privacy engineering problem that most organizations solve by restricting access rather than transforming data.
Our Anonymization API goes beyond simple pattern matching. It detects PII in context, understands entity relationships, and applies mathematically rigorous privacy transformations that preserve analytical utility.
Names, addresses, Social Security numbers, medical record numbers, financial identifiers, email addresses, phone numbers, dates of birth, IP addresses, biometric identifiers, and dozens more. Context-aware detection means “Jordan” is flagged as a person name in a patient record but recognized as a country in a geography dataset.
Transform quasi-identifier fields so every record is indistinguishable from at least k-1 others. L-diversity ensures that sensitive attributes maintain sufficient variety within each equivalence class, preventing attribute disclosure even when an adversary knows the individual is in the dataset.
Add calibrated noise to query results or dataset exports so that no individual record can be reverse-engineered from the output. Mathematically proven privacy guarantees that hold regardless of what auxiliary information an adversary possesses. Epsilon and delta parameters let you tune the privacy/utility tradeoff precisely.
Replace direct identifiers with consistent pseudonyms that preserve referential integrity across datasets. The same individual gets the same pseudonym in every table, so joins and longitudinal analysis work — but the mapping is stored separately and access-controlled. Meets GDPR Article 4(5) definition of pseudonymization.
When the goal is removal rather than transformation, our API redacts PII completely or replaces it with masks that preserve format structure. A redacted phone number shows [PHONE]; a masked SSN shows ***-**-1234. Configurable per entity type, so you can redact names but mask account numbers in the same document.
PII appears in every language your organization processes. Our models detect entities across 30+ languages natively — not through translation layers that lose context. A Japanese patient name, a German address, and a Brazilian tax identifier are all detected with the same accuracy as English equivalents.
Submit a document, text block, or structured data record. Our pipeline detects every PII entity, applies the privacy transformation you configured for each entity type, and returns the anonymized output along with a detailed report of what was found and how it was handled.
The entire process is deterministic for a given configuration: the same input with the same settings always produces the same output. This reproducibility is essential for audit trails and regulatory documentation — you can demonstrate exactly what your pipeline does to any regulator who asks.
Submit text to our live demo and watch PII detection and transformation in real time. No sign-up, no credit card, no data retention.
