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By Protecto
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.
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By Amar Kanagaraj
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.
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By Amar Kanagaraj
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.
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By Amar Kanagaraj
“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.
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By Amar Kanagaraj
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.
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By Amar Kanagaraj
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.
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By Shankar Rajamani
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.
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By Amar Kanagaraj
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.
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By Amar Kanagaraj
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.
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By Amar Kanagaraj
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.
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By Protecto
The goal of AI data security isn't to remove data. It's to control it. For every request, Protecto decides in real time whether data is allowed, masked, anonymized, rewritten, or selectively revealed. Those decisions change with the user, the task, the destination, and the policy.
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By Protecto
A sales executive can view contracts, pricing, discounts, and account details. Ask an AI assistant to summarize a customer meeting, and it needs the customer name. Ask the same assistant to draft a marketing email, and it must not include confidential pricing or discounts. Same user, same data, different task, different answer.
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By Protecto
Your AI gateway doesn’t solve this. Neither does your identity platform. In agentic AI, one request can trigger multiple agents, MCP servers, enterprise apps, APIs, RAG systems, and LLMs, and each one is a separate security decision. The old question was “Can this user access this application?” The new question is “Can this task use this piece of enterprise data?”
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By Protecto
Your AI gateway and identity platform don’t decide whether a task can use a piece of enterprise data. In agentic AI, one request can trigger multiple agents, MCP servers, APIs, RAG systems, and LLMs, and each one is a separate security decision. This video explains why enterprise AI needs a runtime data control plane. It covers why identity alone can’t make the call, why masking PII isn’t enough when business context is sensitive, and how Protecto applies allow, mask, anonymize, rewrite, or selective reveal decisions across the AI workflow. It ends with a Fortune 100 multi-agent example.
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By Protecto
What if masking sensitive data actually broke your AI application? Most AI security strategies start with a simple rule: mask sensitive information before it reaches the LLM. But real AI workflows are more complex. An AI agent might need to search a CRM, retrieve a contract, check account details, call external tools, and generate a response. Each step may require a different level of access to sensitive data.
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By Protecto
Can enterprises use powerful AI models with sensitive data without forcing everything into a self-hosted model? That's the problem this demo explores. In this session, we demonstrate Protecto Privacy Gateway integrated with LibreChat and show how sensitive enterprise data can be protected across AI chat, RAG, agents, MCP/tool calls, and long-running conversations.
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By Protecto
Most DLP tools can mask a PII value on the way into a model. Almost none of them can make that value usable again when the AI needs to call a tool with it, so the masking breaks the workflow instead of protecting it. This is Protecto Privacy Gateway for AI Chat, live in a self-hosted LibreChat deployment. Watch what happens when a masked account number needs to resolve at a tool boundary — and comes back correct.
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By Protecto
Sensitive data detection was much easier when information lived inside structured databases. Tables, columns, field names, and predictable data types gave security teams a clear map of where sensitive information lived. But when sensitive data moved into documents and PDFs, that map disappeared. Names, addresses, phone numbers, credit card numbers, and other sensitive information could be buried inside natural language.
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By Protecto
Most healthcare AI systems rely on Retrieval-Augmented Generation (RAG) and vector databases to search millions of insurance claims, physician notes, lab reports, billing documents, and customer records. But what happens before those documents become searchable? In this Short, we explain how embedding models convert raw healthcare documents into vectors—and why patient names, medical record numbers (MRNs), diagnoses, and Social Security numbers can end up stored inside your vector database if data isn't protected before ingestion.
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By Protecto
Why Your Healthcare RAG Pipeline Is Leaking PHI Most healthcare organizations believe their AI assistant is secure once they restrict who can log in. Unfortunately, that's only part of the story. Modern healthcare AI applications rely on Retrieval-Augmented Generation (RAG), where patient records, physician notes, insurance claims, and medical documents are embedded into vector databases to power intelligent search.
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By Protecto
Know the challenges associated with managing data privacy and security, and the capabilities that organizations need to look for when exploring a data privacy and protection solution.
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By Protecto
Improve your organization's privacy and security posture by automating data mapping. Read on to understand some best practices for privacy compliance.
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By Protecto
Protecto can help improve your privacy and security posture by simplifying and automating your data minimization strategy. Read on to know more.
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Easy-to-use API to protect your enterprise data across the AI lifecycle - training, tuning/RAG, response, and prompt.
Protecto makes all your interactions with GenAI safer. We protect your sensitive data, prevent privacy violations, and mitigate security risks. With Protecto, you can leverage the power of GenAI without sacrificing privacy or security. If you are looking for a way to make your GenAI interactions safer, then Protecto is the solution for you.
Data protection without sacrificing data utility:
- Achieve Compliance And Mitigate Privacy Risks: Preserve valuable information while meeting data retention regulations.
- Embrace Gen AI Without Privacy or Security Risks: Harness the power of Gen AI, ChatGPT, LLMs, and other publicly hosted AI models without compromising on privacy and security.
- Share Data Without Sacrificing Compliance: Comply with privacy regulations and data residency requirements while sharing data with global teams and partners.
- Ensure The Security Of Your Data In The Cloud: Protect your sensitive and personal data in the cloud. Gain control over your cloud data.
- Create Synthetic Data: Harness real-world data for testing without compromising on privacy or security.
- Achieve Data Retention Compliance with Anonymisation: Simplify compliance efforts and safeguard sensitive data.
Protect your enterprise data across the AI lifecycle.