The Application Security Podcast

Brad Geesaman - Redefining AppSec with AI: Shrinking Toil, Expanding Impact - How LLMs are able to reduce toil in triage-heavy AppSec workflows

Chris Romeo and Robert Hurlbut Season 12 Episode 20

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0:00 | 42:19

AppSec teams are drowning in repetitive triage while the work that requires judgment keeps piling up. Brad Geesaman, Principal Security Engineer at Ghost Security, explains how large language models can shrink that toil without handing security decisions to an unreliable black box. He walks through using LLMs for classification, evidence gathering, and contextual analysis, with humans retaining final authority. Brad and Chris examine prompt engineering, trust, market disruption, and the limits of incumbent tools built around producing ever-larger finding queues. They also explore AI-assisted remediation, code drift, and the changing day-to-day work of AppSec engineers. The result is a pragmatic model for gaining leverage from AI while preserving the expertise, accountability, and skepticism that effective security still demands.

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Connect with Brad Geesaman:
Brad Geesaman on LinkedIn
Ghost Security Reaper

Mentioned in this episode:
Ghost Security
Reaper
Security Compass
OWASP ZAP
Burp Suite Professional
SQL Slammer
Code Red
Nimda
Exodus Communications
NetWitness

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Chapters:
00:00 Meet Brad Geesaman
03:01 What toil means in AppSec
05:20 Why triage drains security teams
06:13 Where AI can create leverage
09:29 Does an LLM need custom training?
11:51 Prompt engineering for useful results
13:33 Humans remain at the center
15:23 Trusting probabilistic systems
19:30 A seismic shift in AppSec tooling
20:18 Escaping the pile of findings
24:00 How incumbent vendors are responding
25:46 Why platform shifts leave openings
28:31 The AppSec engineer's changing day
33:04 Moving from triage to code changes
35:36 AI-generated code and application drift
38:49 What Brad hopes comes next
41:51 Closing thoughts

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