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BEGIN:VEVENT
UID:pretalx-2026-FCKFCD@cfp.nsec.io
DTSTART;TZID=EST:20260515T150000
DTEND;TZID=EST:20260515T153000
DESCRIPTION:Security vulnerabilities often stem from misusing operating sys
 tem or third-party APIs. The traditional solution—wrapping dangerous fun
 ctions with secure-by-default frameworks—works beautifully in theory but
  fails at scale. How do you migrate thousands of call sites across multipl
 e applications when each requires understanding developer intent and choos
 ing appropriate security controls?\n\nFor over a decade\, Meta's security 
 team built approximately 15 secure-by-default frameworks for Android\, eac
 h designed to prevent specific vulnerability classes. These frameworks wer
 e elegant\, well-designed\, and... underutilized. The deployment bottlenec
 k wasn't technical merit\; it was practical scalability. Manual migration 
 was impossibly slow. Deterministic static analysis required massive engine
 ering investment and still struggled with precision. Simple pattern matchi
 ng was fast but dangerously error-prone.\n\nThis talk reveals how we solve
 d this problem using generative AI\, specifically Llama models\, to automa
 tically suggest and apply security framework migrations across Meta's code
 base. The solution isn't just faster—it unlocks scalability that was pre
 viously impossible.
DTSTAMP:20260507T211835Z
LOCATION:Ville-Marie
SUMMARY:Teaching AI to Secure Code: How LLMs Deploy Security Frameworks at 
 Scale - tanu jain
URL:https://cfp.nsec.io/2026/talk/FCKFCD/
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