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DTSTART:20241027T030000
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DTSTART:20250330T030000
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SUMMARY:When Data Talks\, We Let AI Listen - Léa ULUSAN
DTSTART;TZID=Europe/Paris:20250619T170000
DTEND;TZID=Europe/Paris:20250619T170500
DTSTAMP:20260907T125542Z
UID:pretalx-bsidesluxembourg-2025-EHAWQX@pretalx.com
DESCRIPTION:At CIRCL\, we're working to make sense of the ever-growing str
 eam of vulnerability data—structured\, unstructured\, and everything in 
 between. From public advisories\, the data is rich but often inconsistent\
 , fragmented\, and difficult to navigate.\n\nTo help tackle this\, we’re
  using Natural Language Processing (NLP) and large language models (LLMs) 
 to extract insights—like estimating vulnerability severity from free-tex
 t descriptions when no CVSS score is available.\n\nOur custom NLP model is
  trained on real-world data using our own infrastructure and updated regul
 arly to reflect new trends. We’ve also released everything we’ve built
 : from raw datasets and training code to the final models\, all available 
 on Hugging Face. And with our ML-Gateway tool\, anyone can easily retrieve
  an AI-generated severity score—no structured metadata required.\n\nThis
  session will walk through:\n\n-The challenges of working with messy\, rea
 l-world vulnerability data\n-How we’re using AI to structure\, score\, a
 nd make sense of it\n-What we've built\, what we’ve learned\, and what
 ’s next\n\nIt’s an inside look at how we're combining human context wi
 th machine learning to improve how we understand and act on vulnerability 
 information.
LOCATION:Main Stage
URL:https://pretalx.com/bsidesluxembourg-2025/talk/EHAWQX/
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