LLMs have made flagging possible weaknesses cheap, but proving which ones are real, exploitable vulnerabilities is not. Security teams are testing different automation strategies to accelerate validation and remediation, but every unproven fix can introduce its own vulnerabilities in turn.
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 without the right supervision amplifies that risk rather than reducing it.
We'll walk through an alternative: how real companies are using AI to prove each vulnerability is real, generate the steps to reproduce it, and propose the fix, so the engineer can approve a patch fast instead of re-investigating it. That keeps the speed gains without handing production control to the model.
- Framework for evaluating vendor claims about "fully autonomous" AppSec
- 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
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