Daxa Recognized as key vendor in Gartner's 2025 AI TRiSM Market Guide Read More

Build Secure AI Experiences

Fastest way to ensure your AI powered applications and agents respect data privileges, compliance, and security from day one.

Request a demo

Open Source

Power AI Apps And Agents With Real Enterprise Data

Integrating Pebblo into your AI app and agent is simple. Just a few lines of code or easy configuration of your MCP client is all it takes to unlock secure, compliant access to real enterprise data.
‍
AI builders can focus on innovation, while Pebblo ensures data access stays secure and compliant preventing oversharing, insider risk, and policy violations from day one.
‍
Governance and security teams get the visibility and control they need to confidently approve AI for production using real company data to accelerate value and deliver ROI.

// Pebblo

Pebblo Enterprise Platform

The Pebblo Enterprise Platform connects your AI apps and agents with company data sources while enforcing access control, governance & security

Safe Connectors

Permissions-aware, classifier-enabled pipelines to all your enterprise data

Safe Retriever

Ensure only role-appropriate, compliant, and safe data is fetched from VectorDBs

Safe MCP

Empower your MCP enabled AI agents with identity, policy, and security driven unified data access - without worrying about sprawl of untrusted MCP servers

Safe Prompt

Removes sensitive data and prevents injection attacks.

Safe Inference

Route to policy approved, authorized AI Model/Adapter for inference.

Data Governance and Security

Monitoring, Policy, and Security for AI apps, retrieved data, and user behavior

// Pebblo SDK

How AI Developers Use Pebblo

Register your AI app or agent with Pebblo and then follow these three simple steps to build secure and compliant AI experiences with real company data:

Pre-Retrieval

Sanitize user prompts before processing

Prevent data leaks, abuse, and attacks

sanitized_prompt, status = pb.safeprompt (query, user, [governance_filters])

Data Retrieval

Retrieve role appropriate context data from VectorDB

sanitized_chunks, status = pb.search (query, user, [search_options], [data_source_list],[governance_filters])

Post-Retrieval

Generate response from one of the policy approved AI models

‍

response_handle, status = pb.infer (context, user, [opt],[governance_filters])

// Pebblo MCP

How AI Agents Use Pebblo

Your AI agents, both in-house and commercial, are using MCP to plug in to enterprise systems. Pebblo MCP allows those agents to easily access enterprise data with authorization and policy compliance, while protecting them from suspicious command injections. Without worrying about the sprawl of untrusted MCP servers. Powerful protection to limit the blast radius from misbehaving or compromised agents.

// Pebblo DASHBOARD

How Data Governance & Security Teams Use Pebblo

Discover

Unified visibility into AI apps and agents, models, and ingested data. Auditability of AI data retrievals, user and agent activities.

Govern

Deterministically ensure AI apps and agents respect data permission with added semantic, confidentiality, and compliance policy enforcement.

Secure

Prevent threats from exploiting mis-provisioned access or hijacking AI agents to extract sensitive enterprise data.

// Why Pebblo?

Move AI From POC To Production

Pebblo brings real time data intelligence to AI ingested data classifying, tagging, and enforcing access as data flows into retrieval systems like RAG, for model grounding context, or MCP for agentic data access. At AI runtime, it applies deterministic controls to precisely filter what context is passed to the AI app or agent, ensuring sensitive data stays protected and only the right information is surfaced.

This shift-left enforcement is what AI firewalls miss and it’s what enterprises need to safely unleash AI on real business data, move beyond POCs, and unlock ROI at scale.

// OUR APPROACH

Shift-Left Governance and Security

AI firewalls try to filter model outputs after the fact, without knowing what enterprise data went in to generate that output. Without data context, they're stuck guessing what a user should or shouldn't see. That's not governance, it's gambling. 
‍
With Pebblo’s shift-left approach, we can flip the script. Our Safe Connectors extract fine grained permissions from enterprise systems, and also capture the data’s classification, confidentiality levels, and risk of injection attacks. And our Safe Retriever enforces them before the AI model sees any data. The result? Only authorized, compliant, and secure context reaches the AI app or agent at runtime, thus deterministically preventing data exposure and compliance violations, while protecting the AI from compromise.