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Zero-Trust AI Framework

Open-Source Security Framework for AI Agents

 

Overview

 

Zero-Trust AI is an open-source framework designed to help developers build, evaluate, and secure specialized AI agents using zero-trust security principles. This project applies established cybersecurity practices to the emerging challenges of agentic AI systems, where autonomous agents communicate and collaborate in potentially hostile environments.

 

Website: zero-trust.ai
Repository: github.com/zero-trust-ai/framework


License: MIT

 

Status: Stage 0 - Foundation (Q1 2026)

The Problem

 

As AI systems evolve from isolated chatbots to interconnected autonomous agents, traditional security approaches are inadequate:

 

AI agents communicate through protocols like Model Context Protocol (MCP)

 

  • Multi-agent systems create complex trust boundaries
  • A compromised agent can affect entire networks of AI services
  • Existing security frameworks weren't designed for AI-specific threats
  • Developers lack tools tailored to agentic architecture security

 

Attack vectors specific to AI agents include:

 

  • Prompt injection attacks
  • Data exfiltration through outputs
  • Privilege escalation via agent capabilities
  • Agent-to-agent compromise
  • Training data poisoning

Project Goals

 

Primary Mission

 

Democratize AI security by creating accessible, transparent, and community-driven security tools for AI agents.

 

Specific Objectives

 

  • Educational: Teach zero-trust principles for AI through staged learning
  • Practical: Provide working code and reusable templates
  • Open: Build community-driven security standards
  • Impactful: Enable widespread secure AI agent deployment

Staged Development

 

The project follows a 5-stage roadmap designed for progressive learning and building:

 

Stage 0: Foundation (Q4 2025 - Current)

  • Documentation and threat modeling
  • Architecture design
  • Community establishment

 

Stage 1: Guardian Core (Q1-Q2 2026)

  • Basic security evaluation engine
  • Prompt injection detection
  • Input/output validation
  • Logging and monitoring

 

Stage 2: MCP Security (Q2 2026)

  • Protocol analysis and validation
  • Agent-to-agent security
  • Trust management

 

Stage 3: RAG Integration (Q2-Q3 2026)

  • Dynamic security policies
  • Threat intelligence retrieval
  • Adaptive controls

 

Stage 4: Multi-Agent Security (Q3 2026)

  • Behavioral profiling
  • Anomaly detection
  • Reputation systems

 

Stage 5: Production Hardening (Q4 2026)

  • Performance optimization
  • Enterprise deployment templates
  • Compliance and audit features
  • Educational Focus
  • Each stage includes:
  • Comprehensive documentation
  • Working code examples
  • Security best practices
  • Hands-on tutorials
  • Clear explanations of concepts

 

This approach makes AI security accessible to developers without formal security backgrounds.

Technology Stack

 

Core Framework

  • Python 3.9+
  • Pydantic for data validation
  • Structured logging with structlog
  • Comprehensive testing with pytest
  • AI/ML Integration (Future stages)
  • LLM API support (OpenAI, Anthropic, etc.)
  • Vector databases (ChromaDB, Pinecone)
  • Embeddings and semantic analysis

 

Deployment (Stage 5)

  • Docker containerization
  • Kubernetes orchestration
  • Terraform infrastructure as code
  • Prometheus/Grafana monitoring

Licensing and Openness

 

License: MIT

 

The MIT License was chosen to maximize impact and thought leadership:

 

Why MIT?

 

  • Maximum Simplicity
  • Shortest, clearest open-source license
  • Everyone understands it immediately
  • No corporate legal friction whatsoever
  • Thought Leadership Focus
  • Shows complete confidence in expertise over code ownership
  • Demonstrates generosity and commitment to community
  • Zero Barriers to Adoption
  • Any company can use immediately without legal review
  • Perfect for educational and research purposes
  • Enables fastest possible ecosystem growth
  • Allows derivative works under any license

 

Industry Standard

Used by jQuery, Rails, Node.js, React, and countless others

Most popular open-source license

Maximum trust and recognition

 

Strategic Alignment

 

MIT licensing reinforces our thought leadership strategy:

 

  • Value is expertise, not code hoarding
  • Impact through ubiquity - more users = more influence
  • Educational mission - teaching matters more than control
  • Community building - lowest barrier to contribution
  • Reputation building - known for generosity and knowledge sharing

 

The framework is completely open-source with absolute minimal restrictions, perfectly aligned with our mission to democratize AI security and establish thought leadership in the space.

Competitive Landscape

 

While various AI security tools exist, Zero-Trust AI is unique in:

 

  • Zero-trust focus: Explicitly designed around zero-trust principles for AI
  • Agent-specific: Built for autonomous agents, not just chatbots
  • Open-source: Fully transparent with permissive licensing
  • Educational: Designed to teach, not just provide tools
  • Comprehensive: End-to-end security from input to multi-agent coordination

 

Existing tools typically focus on:

  • Prompt injection only (narrow scope)
  • Closed-source solutions (trust/vendor lock-in issues)
  • Research projects (not production-ready)
  • General ML security (not agent-specific)

The Solution

 

Zero-Trust AI provides a comprehensive framework that extends traditional zero-trust principles to AI agents:

 

Core Principles

 

  • Never trust, always verify - No implicit trust between agents or in user inputs

  • Assume breach - Design systems resilient to individual agent compromise

  • Least privilege access - Agents receive only minimum necessary permissions
  • Continuous monitoring - Real-time evaluation of agent behavior
  • Context-aware security - Dynamic policy enforcement based on behavior patterns

 

Key Components

  • Guardian Security Engine
  • Real-time evaluation of agent interactions
  • Prompt injection detection
  • Behavioral analysis and anomaly detection
  • Explainable security decisions
  • MCP Security Layer
  • Secure agent-to-agent communications
  • Protocol-level threat detection
  • Trust boundary enforcement
  • RAG-Powered Policies
  • Dynamic, updatable security policies
  • Threat intelligence integration
  • Adaptive security controls
  • Multi-Agent Orchestration
  • Agent behavior profiling
  • Coordinated attack detection
  • Reputation management