Granting autonomous AI systems the capacity to read source repositories, call shell tools, and execute scripts inside a corporate network introduces massive new security vulnerabilities. From indirect prompt injections hidden in untrusted git branches to excessive tool execution permissions, a misconfigured agent pipeline can quickly turn into a critical internal threat vector.
This security architecture session maps out a complete Zero-Trust defensive framework for hosting and running agentic automation systems. Using a live multi-agent software factory as our blueprint, we explore how to build secure, isolated tool execution sandboxes using modern containerization and network gating. We cover how to securely authenticate machine-to-machine tool access via Model Context Protocol (MCP), manage non-human workload identities, and construct automated verification layers that quarantine malicious agent behavior before it touches production infrastructure.
What you will learn:

- Threat Modeling Agents: Analyzing vulnerabilities unique to autonomous systems, focusing on prompt injection and tool-abuse vectors.
- Sandboxed Execution: Engineering isolated runtime perimeters around automated tools to minimize an agent's network blast radius.
- Identity & Logging: Best practices for authenticating, authorizing, and auditing agentic machine-to-machine actions across corporate networks.