Open Source Summit India 2026

You wake up, open X, and see the post: new model just dropped. By the time you finish your coffee, it’s installed, pointed at your repo, and already making “helpful” suggestions. It works. It feels safe. But what did you actually just hand over access to, and how would you even notice if it crossed a line?

In this talk, we’ll unpack why testing every new AI model or coding assistant can quietly introduce serious security risks. Using real examples inspired by incidents like OpenClaw, we’ll show how AI systems can access internal data, influence code paths, and create vulnerabilities without ever asking for permission.

We’ll discuss why experimentation without guardrails can bypass existing security controls and normalize unsafe patterns. Most importantly, we’ll focus on what teams can do instead. The session will include a live walkthrough using the Kubernetes agent-sandbox project to demonstrate how AI agents can be isolated, sandboxed, and constrained with clear boundaries.
By the end, attendees will leave with practical approaches to evaluate AI tools safely, apply guardrails, and enable innovation without putting code or data at risk.