Build Secure RAG for Enterprise Agents.
Load enterprise data into secure, policy-safe RAG for AI agents, with ingestion, retrieval, masking, access control, audit logs, and compliance built in.​
- Trusted by Engineering Teams Building AI at Scale
The Problem
Building Secure RAG
Should Not Take Months​
Enterprise teams need more than a vector database. They need ingestion, chunking, retrieval, masking, access control, audit logs, and compliance controls working together before agents can safely use sensitive data.​
RAG Infrastructure Complexity​
Teams spend months stitching together document ingestion, chunking, embedding, vector search, retrieval logic, and agent APIs before the first secure answer is ready.​
Sensitive Data Exposures
Enterprise documents contain PII, PHI, customer data, contracts, pricing, and confidential business context. Without protection, RAG can expose sensitive data in prompts, retrieved context, and responses.​
Access and Compliance Risk​
Once data is chunked, indexed, and retrieved, original source permissions are not enough. RAG needs policy- aware controls, audit logs, and controlled unmasking built into the retrieval layer.​
How Protecto Works
How GPTGuard Secure RAG Works in 5 Steps
Load your enterprise data into GPTGuard. It protects, indexes, retrieves, and serves policy- safe context to AI agents and applications.​
Load Enterprise Data​
Upload documents, files, and knowledge sources your AI agents need to use.​
Protect Before Indexing​
GPTGuard detects PII, PHI, regulated identifiers, and sensitive business context, then masks or transforms protected data before it becomes part of RAG.​
Index for Secure Retrieval​
Protected content is chunked, indexed, and prepared for accurate retrieval without exposing raw sensitive data.​
Retrieve Policy-Safe Context​
Built-in RAG retrieval returns the most relevant protected context based on user, role, task, and policy.​
Power AI Agents​
Agents receive secure, useful context for answers, summaries, and workflows. Authorized users can access original sensitive values through controlled unmasking when needed.​
Why Protecto
Why Teams Choose Secure RAG powered by​GPTGuard
Most teams do not want to build secure RAG from scratch. GPTGuard brings ingestion, data protection, retrieval, access control, audit logs, and controlled unmasking together in one ready-to-use RAG system.
01
Turnkey Secure RAG​
Load data, protect it, index it, retrieve it, and serve it to agents without building the full RAG stack yourself.​
02
Protected Retrieval​
GPTGuard retrieves useful context from protected data while reducing exposure of PII, PHI, and sensitive business information.​
03
Agent-Ready Controls​
Simple API or MCP integration. Audit logs and controlled unmasking are built in so agents can use enterprise data safely.​
Capabilities
Everything You Need for Secure RAG​
GPTGuard gives teams the core components needed to make enterprise data usable by AI agents without building separate ingestion, retrieval, protection, and compliance systems.​
Document Ingestion​
Upload enterprise documents and knowledge sources into a ready-to-use RAG system.​
Sensitive Data Detection​
Detect PII, PHI, regulated identifiers, and sensitive business context before data is used by AI.​
Context-Preserving Masking​
Protect sensitive data while preserving enough structure and meaning for accurate retrieval and AI responses.​
Built-In RAG Retrieval​
Chunk, index, and retrieve relevant protected content for enterprise agents and AI applications.​
Built-In CBAC​
Identify and enforce context-based sensitive data controls, so users, agents, and tasks retrieve only the document context they are allowed to use.​
Audit and Controlled Unmasking​
Track every sensitive data action and reveal original values only when authorized.​
Success Story
How a Large Insurance Provider Used Secure RAG to Tackle $200B in Medical Overbilling
Healthcare. Compliance. AI.
Building a Privacy-Preserving Fraud Detection System
Medical billing errors (upcoding, unbundling, incorrect coding) cost the healthcare system hundreds of billions annually. A leading US insurance provider wanted to apply LLMs to detect discrepancies between clinical notes and billing codes at scale.
The problem: every claim record contained PHI. Running that data through an LLM without masking it first would break HIPAA. The team needed a way to let the AI see the clinical patterns without seeing the patients.
- Protecto scanned and masked all PHI within the clinical notes dataset before ingestion into the data pipeline.
- Masked notes were vectorized and stored. LLMs ran similarity searches to flag billing discrepancies.
- Fraud analysts accessed original data via Protecto's secure unmask workflow when investigation required identifiable records.
- Full HIPAA compliance maintained throughout. LLM accuracy unaffected by the masking process.
Security and Compliance
Compliance isn't a checkbox.
It's built into the platform.
Every Protecto deployment includes audit logs for every scan, mask, and unmask event. We sign BAAs for HIPAA. We support data residency and air-gapped deployments for strict sovereignty requirements.
Adopt AI. Not AI Risk.
Your RAG pipeline is already in production. The question is whether your data privacy is. Protecto takes minutes to integrate.
- SOC2 Certified
- HIPAA Compliant
- GDPR Ready
- ISO 27001