LLMs have made bug discovery nearly a commodity capability. What was groundbreaking a year ago is now standard, and security teams are finding more true positive vulnerabilities than they can realistically triage. Vendors have responded with a new pitch: fully autonomous remediation, where AI doesn't just find the bug, it ships the fix too. If you're evaluating one of these tools, or fielding pressure to, this session will help you separate the parts of that pitch worth taking seriously from the parts that could put your production environment at risk.

Autonomous remediation is appealing when your team is buried in a confirmed-bug backlog. But letting an AI agent independently deploy code to production breaks from decades of change management practice, and the risk isn't hypothetical: unsupervised AI agents have already caused real production outages acting on their own. In complex enterprise environments, where one local patch can ripple into consequences elsewhere, full autonomy amplifies that risk rather than reducing it.

We'll walk through an alternative: using AI to compress discovery, validation, and prioritization from hours to minutes, while keeping a security engineer in the loop to review and approve every patch before it ships. That keeps the speed gains without handing production control to the model. We'll also show what this looks like in practice.

You'll leave this session with a:

- Framework for evaluating vendor claims about "fully autonomous" remediation

- Clearer picture of why human review matters most in complex, interdependent systems

- Practical model for using AI to speed up triage without giving up control