AI Breaks Trust. Operant Helps Define It.
Get hands-on with Operant’s AI Trust Fabric, a safe environment to explore how trust, security, and data governance work for agentic AI in practice.
- Define and test trust rules
- Connect agents, users, and data using Model Context Protocol (MCP)
- Explore visibility, policy enforcement, and auditability
Over 85% of AI proof-of-concept (POCs) never reach production not because models fail, but because connecting them to enterprise systems securely, privately, and compliantly is hard.
The Secure AI Sandbox lets you experience how Operant governs AI behavior at runtime. You can connect AI agents, users, and data, enforce trust policies, and evaluate real-world impact, all in a safe, isolated environment. Whether you’re starting a pilot or preparing to scale, the Sandbox gives you a risk-free way to understand what it takes to make AI enterprise-ready.
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Why It Matters: Accurate ROI for AI Projects
- POCs answer the “What”: what impact does AI have on a specific use case.
Example: This task used to take 15 hours. Now it takes 20 minutes
- Pilots answer the “How”: how will that AI use case actually work inside your enterprise.
Example: How do we connect the data source? Who has access? What’s the compliance risk?
The Secure AI Sandbox for MCP gives you both. We make it fast to set up and test workflows securely, so you can validate technical performance and operational reality before taking your AI project to production
The Result? Faster, secure pilots that turn into real deployments with less risk, lower cost, and a clear path from proof-of-concept to production.
What You Can Do in the Sandbox:
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Simulate trust flows for AI agents using Model Context Protocol (MCP)
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Define and test access policies across teams, clouds, and domains
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Authenticate and secure connections using certificate-based mTLS transport
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Track and audit agent behavior with full identity and access logging
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Enforce data privacy with redaction and data visibility tools
Ready to Go Beyond the Pilot?
