Prahathess Rengasamy
Prahathess Rengasamy is a security engineer at xAI focused on security engineering and automation, striving to enhance efficiency and robustness across various security domains. Previously securing products and clouds at Block, Apple, and Credit Karma, he now focuses on scaling security for the AI era.
Session
Track
Centaur Track (20-minute Lightning Talk)
AI coding assistants now generate the majority of infrastructure code in many organizations. Traditional paved road strategies—approved Terraform modules, policy-as-code gates, security training—targeted developer cognition. But in agentic workflows, the developer provides intent while the model does the reasoning. The thinker changed.
This creates an opportunity: the model's context window is programmable in ways developer brains never were. By constraining what tools an agent can call and what information shapes its reasoning, organizations can embed security guardrails directly into the system doing the thinking.
This talk introduces a reference architecture using Model Context Protocol (MCP) to make AI agents work through paved roads rather than around them. The MCP server exposes approved Terraform modules from an internal registry, enforces OPA policies with structured output designed for agent consumption (severity, remediation hints, auto-fix eligibility), and implements risk-scored exception handling that routes low-risk deviations to audit logs while escalating high-risk requests to humans.
In a live demo, a natural language infrastructure request will flow through the full pipeline: module retrieval → Terraform generation → policy check → auto-remediation of fixable violations → risk scoring → exception request with Linear ticket creation → compliant PR with reasoning trace. The demo will show policy violations detected and resolved in seconds with zero human intervention for routine issues.