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PDF Data Masking: The Missing Security Step in Document AI

PDF data masking is the security step most document AI stacks skip. Learn why it has to happen before documents reach RAG, agents, or model training, and how Protecto’s open-source PDF masking project handles it today....

Data Policy in the RAG Pipeline: Stop 30 Years of Exposure

Every company is turning its document archive into a knowledge base for AI. Here's what changes when Protecto enforces data policy in the RAG pipeline, protected at ingestion, policy-rendered at retrieval, so the same precedent looks different to an adjuster and a customer....

Data Policy at the Agent Gateway: Stop 6 Leaky Handoffs

The riskiest moment in a multi-agent system is the handoff. Here's what changes when Protecto enforces data policy at the agent gateway, minimum data out, no internal analysis leaking back into customer-facing agents....

Data Policy at the MCP Gateway: Stop 2 Blind Spots

Your agents' tools see more than your agents do. Here's what changes when Protecto enforces one data policy at the MCP gateway, every tool call checked both ways, the way out and the way back....

Data Policy at the AI Gateway: Stop 12 Scrubbing Rules

Every team is calling an LLM now support, sales, summarization, and features security hasn't even seen yet. Here's what changes when Protecto enforces one data policy at the AI gateway all of that traffic already crosses....

Enforcing AI Data Policy at the API Gateway

Your AI agents all call the same internal APIs, and those APIs return everything. Here's why enforcing data policy at the API gateway scales better than building filtering into every agent you ship....

Protecting Sensitive Data Inside an AI Agent

An AI agent needs the full case file to do its job, but that file holds things nobody downstream should see. Here's how protecting sensitive data inside the agent's own workflow turned a blocked rollout into a production one....

How Protecto’s Privacy Gateway protects data inside LibreChat

A technical walkthrough of how Protecto Privacy Gateway for AI Chat masks prompts, resolves tool calls, and protects retrieved context inside LibreChat, turn by turn....

Six Integration Patterns for Enforcing Data Policy Across Enterprise AI

Enterprise AI touches sensitive data in six architecturally different places, not one. This series shows the integration pattern for enforcing data policy at each one, from inside the agent to the RAG pipeline...

Secure Data Sharing: Best Practices for Privacy and Compliance

Discover secure data sharing best practices for protecting sensitive business information. Learn how encryption, tokenization, masking, and access controls enable secure data exchange across organizations, cloud platforms, and AI workflows....

AI Model Governance: Framework, Roles, Controls and Implementation Checklist

Explore AI model governance, including frameworks, policies, best practices, roles, controls, and a practical checklist for managing AI models throughout their lifecycle....

AI Acceptable Use Policy: Template, Examples and Implementation Guide

Learn how to create an AI acceptable use policy with practical templates, examples, and implementation steps. Discover how enterprises define AI usage rules, protect sensitive data, and enable secure generative AI adoption....

What Is Data Loss Prevention (DLP)? Types, Use Cases, and Best Practices

Understand data loss prevention (DLP), its meaning, types, use cases, and how modern DLP solutions help enterprises discover, monitor, and protect sensitive data across cloud, AI, and enterprise workflows....

Principle of Least Privilege: Meaning, Examples, and Implementation for AI Agents

Understand the principle of least privilege, its access control model, real-world examples, and how enterprises implement least privilege for securing AI agents, sensitive data, and modern applications....

Why Is a Reranker Needed in RAG If We Have a Retriever?

Enterprise RAG pipelines have two stages recall and precision. The retriever handles recall. The reranker handles precision. Skipping the reranker, or misplacing security controls around it, is where most accuracy and data exposure problems begin....
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