Cipher monitors your training pipelines in real time — flagging copyright infringement, privacy violations, and regulatory risk before they become lawsuits.
Public web scrapes contain licensed content, news articles, and published work. Training on them without provenance tracking exposes your company to infringement claims. The New York Times vs. OpenAI is just the beginning.
PII, medical records, financial data — all scraped from public sources. GDPR, CCPA, and the EU AI Act require companies to know exactly what personal data enters their models. Manual audits are slow. The data moves faster than the lawyers.
When regulators come — and they will — you need to show exactly which data sources trained which model versions. Most companies have no documentation. Cipher builds that record automatically, continuously.
Cipher integrates with data ingestion pipelines — S3 buckets, data loaders, preprocessing pipelines. No hardware changes required.
For each batch entering training, Cipher runs privacy, copyright, and regulatory classifiers. It scores risk by data source, jurisdiction, and content type.
High-risk data is flagged or blocked before it enters the model. Low-risk data is logged with full provenance — timestamp, source, classifiers triggered.
On-demand audit reports for legal teams, regulators, and partners. Every data decision is documented with evidence, ready for GDPR Article 35 DPIA or EU AI Act Article 11 disclosures.
"We can measure exactly how much private information leaks out of a language model. We've proven it in peer-reviewed papers. The question was never whether the data was leaking — it's whether anyone was watching the door."
— Katherine Lee, co-author, "Extracting Training Data from Large Language Models" (USENIX Security 2020)Cipher is the autonomous monitoring system that was missing. Built by the researchers who documented the problem — designed to close it.
Cipher watches every data point, every batch, every model version — so you can build with confidence instead of liability.