Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Short answer: GDPR does not require personal data to stay in the EU. It lets data leave only with a valid transfer mechanism. Data residency for LLMs comes down to three options: use the EU region of a hosted API, tokenize before the API call, or run the model yourself. Only the third keeps data out of the US in the literal sense. A procurement lead at a European customer sends your team a one-line requirement: “keine Daten in die USA.” No personal data in the United States.

Short answer: GDPR names pseudonymization as a recommended safeguard, in Article 25 and Article 32, and Recital 28 says pseudonymization reduces risk for data subjects. Redaction deletes the value, so a redacted prompt breaks the model’s ability to reason. Tokenization replaces the value with a consistent token, so the model keeps working. Tokenization is a safeguard, not an exemption. A familiar pattern: a team writes a regex, strips emails from the prompts, and calls it done.

Short answer: Do not assume an embedding is anonymous. An embedding can be matched back to the text it came from, and the text is usually stored right next to it. Treat each representation in a RAG pipeline as personal data when it can still be tied to a person, which is most of the time for chunk text. When the source record is deleted, every copy has to go. Your team builds a RAG assistant over HR policy and customer contracts. Documents go in, get chunked, get embedded, and land in a vector store.

“We don’t train on your data” does not mean “we don’t retain it.” Neither promise tells you where your data is processed. Zero data retention (ZDR) closes one of those gaps. It does not close all three.

PII redaction and PII classification get used as if they were the same operation. They are not. PII redaction removes or replaces a value that a system has flagged as sensitive. Classification names what that value actually is, so a policy engine can select a rule for it. In an AI pipeline, the second step is the one that decides whether a policy can run at all. Short answer. PII redaction removes or masks a value a system has flagged as sensitive.

Most evaluations of a PII protection layer ask one question: did it find the sensitive data? That was the right question when data loss prevention meant blocking. A DLP system that found a Social Security number stopped the email, and nothing downstream needed to understand what had been stopped. AI workflows are different. Sensitive data can enter through prompts, tool calls, retrieved RAG context, agent memory, and model outputs, including paths that may sit outside traditional DLP inspection points.

PDF data masking is the security step most document AI stacks skip. Most companies building RAG pipelines, agentic workflows, or document intelligence systems have secured the model, the vector store, and the API. They haven’t secured the step before any of that: the data inside the document itself, before it enters the pipeline. That gap shows up everywhere documents meet AI.

A Technical blog series on where sensitive data moves in an AI workflow, what decisions Protecto makes at each point, and what enterprises get from enforcing policy once instead of everywhere.

A Technical blog series on where sensitive data moves in an AI workflow, what decisions Protecto makes at each point, and what enterprises get from enforcing policy once instead of everywhere.

A Technical blog series on where sensitive data moves in an AI workflow, what decisions Protecto makes at each point, and what enterprises get from enforcing policy once instead of everywhere.