AI in security testing and bug bounty
AI in security testing changes how findings are produced, validated, and paid for. This guide helps AppSec engineers and security leaders examine the difference between more reports and useful evidence. AI Security Table, the roundtable podcast with Chris Romeo, Izar Tarandach, and Matt Coles (formerly The Security Table), debates dynamic testing, automated vulnerability discovery, and bug bounty economics. The episodes also ask when a conventional analyzer or fuzzer is the better fit.
Questions we keep arguing about
Is AI penetration testing replacing DAST?
The hosts trace DAST from scanners to adaptive testing and question where one ends and the other begins. They distinguish finding many issues from following an exploit chain. They also debate uncertainty when scanner errors combine with model-generated analysis. Hear the discussion.
What happens to bug bounty when reports are automated?
The table debates whether internal agents could displace paid outside reports. They examine validation costs and the pressure of increased submission volume. Useful automation still depends on separating real findings from noise. Hear the discussion.
Do we need an LLM for every vulnerability search?
The hosts compare LLM-based vulnerability discovery in a C library with static analysis and fuzzing. They question what the model adds for the specific task. Their discussion asks teams to evaluate conventional approaches alongside AI tools. Hear the discussion.
Episodes
The End of Bug Bounty As We Know It
The hosts examine useful AI-generated Linux kernel reports and the economics of bug bounty. They debate validation, disclosure incentives, and what remains valuable about human research.
Mostly Dead or Mostly Back: The Zombie Resurrection of DAST in an AI World
The table traces the history of dynamic testing and debates adaptive AI penetration testing. The discussion separates scanner usefulness from claims about the future of the DAST market.
The Mythos Problem: When AI Finds Every Vulnerability
Claims of automated discovery raise questions about remediation capacity. The hosts debate whether faster attackers require more agents or a smaller, simpler attack surface.
Is it Necessary? Not everything requires an LLM
The hosts compare AI vulnerability search with static analysis and fuzzing for a C library. They debate whether the added model is necessary for the problem being solved.